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Application of CT-based radiomics in predicting portal pressure and patient outcome in portal hypertension
European Journal of Radiology 126 (2020) 108927
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European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Research article
Application of CT-based radiomics in predicting portal pressure and patient outcome in portal hypertension
T
Yujen Tsenga,b,1, Lili Mac,1, Shaobo Lid,1, Tiancheng Luoa,1, Jianjun Luoe, Wen Zhange, Jian Wanga, Shiyao Chena,b,f,* a
Department of Gastroenterology,Zhongshan Hosptial, Fudan University, China Department of Digestive Diseases, Huashan Hospital, Fudan University, China c Department of Endoscopy Center, Zhongshan Hospital, Fudan University, China d Shanghai Medical College, Fudan University, China e Department of Interventional Radiology, Zhongshan Hospital, Fudan University, China f Evidence-Based Medicine Center, Zhongshan Hospital, Fudan University, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Portal venous pressure Radiomics Esophageal and gastric varices Portal hypertension
Purpose: Portal venous pressure (PVP) measurement is of clinical significance, especially in patients with portal hypertension. However, the invasive nature and associated complications limits its application. The aim of the study is to propose a noninvasive predictive model of PVP values based on CT-extracted radiomic features. Methods: Radiomics PVP (rPVP) models based on liver, spleen and combined features were established on an experimental cohort of 169 subjects. Radiomics features were extracted from each ROI and reduced via the LASSO regression to achieve an optimal predictive formula. A validation cohort of 62 patients treated for gastroesophageal varices (GOV) was used to confirm the utility of rPVP in predicting variceal recurrence. The association between rPVP and response to treatment was observed. Results: Three separate predictive formula for PVP were derived from radiomics features. rPVP was significantly correlated to patient response to endoscopic treatment for GOV. Among which, the model containing both liver and spleen features has the highest predictability of variceal recurrence, with an optimal cut-off value at 29.102 mmHg (AUC 0.866). A Kaplan Meier analysis further confirmed the difference between patients with varying rPVP values. Conclusion: PVP values can be accurately predicted by a non-invasive, CT derived radiomics model. rPVP serves as a non-invasive and precise reference for predicting treatment outcome for GOV secondary to portal hypertension.
1. Introduction Portal hypertension is associated with the most severe complications of liver cirrhosis, including ascites, hepatic encephalopathy and gastroesophageal varices [1]. Among which, gastroesophageal variceal bleeding is considered the most fatal consequence of portal
hypertension. Despite recent advancements in treatment, gastroesophageal variceal bleeding is still associated with a high mortality rate of 10–20 % at 6 weeks [2,3]. According to the Baveno VI Consensus, first line therapy for gastroesophageal varices includes a combination of endoscopic band ligation (EBL), cyanoacrylate injection, and non-selective beta blockers (propranolol or nadolol) [2]. Although
Abbreviations: PVP, portal venous pressure; CT, computer tomography; rPVP, radiomics portal venous pressure; GOV, gastroesophageal varices; AUC, area under curve; KM, Kaplan Meier; EBL, endoscopic band ligation; HVPG, hepatic venous pressure gradient; CSPH, clinically significant portal hypertension; WHVPG, wedge hepatic venous pressure; FHVPG, free hepatic venous pressure; MRI, magnetic resonance imaging; PET, positron emission tomography; NSBB, non-selective beta blockers; TIPS, transjugular intrahepatic portosystemic shunt; HCC, hepatocellular carcinoma; SPSS, spontaneous portosystemic shunt; PVT, portal venous thrombosis; ROI, region of interest; BRTO, balloon-assisted retrograde transvenous obliteration; EIS, endoscopic injection sclerotherapy; PTVE, percutaneous transhepatic variceal embolization; PSE, partial splenic embolization; TAE, transcatheter arterial embolization; MDCT, multi-detector row computer tomography ⁎ Corresponding author at: Department of Gastroenterology, Endoscopy Center, Evidence-Based Medicine Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 0086200032, China. E-mail address: [email protected] (S. Chen). 1 Authors contributed equally to the manuscript and share first authorship. https://doi.org/10.1016/j.ejrad.2020.108927 Received 4 September 2019; Received in revised form 25 February 2020; Accepted 28 February 2020 0720-048X/ © 2020 Elsevier B.V. All rights reserved.
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Fig. 1. Selection of Region of Interest (ROI). Delineation of the liver and spleen as independent ROI in the IBEX software for extraction of radiomic features.
measurements achieved during the TIPS procedure. However, due to the current lack of diagnostic reference for direct PVP, the proposed model is further validated in a group of patients with gastroesophageal varices, to confirm its clinical value in predicting gastroesophageal variceal recurrence. In this study, we were able to predict PVP values via class prediction. Direct PVP measurement is clinically inconvenient to obtain, but can be derived from the easily accessible CT studies with promising accuracy.
repeated endotherapy sessions are widely accepted and practiced, some patients fail to achieve complete variceal obliteration, necessitating an eventual change in course of treatment [2]. Hepatic venous pressure gradient (HVPG) is currently recognized as the “gold-standard” for assessing portal hypertension, which is used to diagnose clinically significant portal hypertension (CSPH, defined as HVPG ≥10 mmHg) and provide prognostic information. Based on the research agenda recommended by the Baveno VI Consensus, portal pressure can also be referenced for therapeutic guidance [2]. However, the invasive nature and high costs of HVPG limits its clinical application as a surveillance modality. HVPG is a surrogate outcome reflecting the difference between wedge hepatic venous pressure (WHVP) and free hepatic venous pressure (FHVP) [6]. The indirect portal venous pressure (PVP) measurement reflected via HVPG may be an inadequate estimate of the actual PVP, hence, disease severity [7–10]. Direct measurement of portal pressure has been previously reported and can be achieved through a direct puncture of the portal vein, either through a percutaneous or transjugular approach. However, the direct approach is associated with a higher risk of complication including hepatic hemorrhage, portal vein thrombosis, and intrahepatic arteriovenous fistula [8,11]. Over the past decade, advancements in medical imaging analysis has grown exponentially. Radiomics is the extraction of quantitative features of tomographic images (CT, MRI, PET) into mineable data and the exploration of their significance to support clinical decision making. Application of high-throughput computing allows for the rapid extraction of innumerable high-dimensional data and pattern recognition. The application of radiomics is boundless, from increasing diagnostic precision, to assessment of prognosis and therapy response [12,13]. Many researchers have explored the utility of radiomics in field of oncology [14,15], such as lesion characterization and prediction of treatment response or metastasis. The utility of radiomics in other clinical specialties warrants further investigation. Radiomics employs available tomographic studies and extracts innumerable quantitative features into mineable high-dimensional data. Apart from providing visual diagnostic information, tomographic images have characteristics that are often neglected by the naked eye. With the rise of machine learning and artificial intelligence in the medical industry, disease diagnosis and management can be revolutionized [13,16]. The general idea of machine learning lies in the ability to predict outcomes from measurable predictors. The present study is aimed at exploring the application of radiomics in liver disease by proposing a noninvasive model for diagnosing PVP in patients with portal hypertension. An experimental cohort was used to construct a PVP prediction model based on CT-derived radiomics and direct PVP
2. Materials and methods 2.1. Experiment cohort Patients admitted to a tertiary medical center from October 2010 to October 2017 for a transjugular intrahepatic portosystemic shunt (TIPS) procedure were eligible for study participation. Inclusion criteria were as follows: 1) Patient diagnosed with portal hypertension, evident by recurrent gastroesophageal variceal hemorrhage, refractory ascites, portal venous thrombosis, hypersplenism, or a combination of symptoms. 2) Patient with available intraoperative direct measurement of portal venous pressure (PVP). Exclusion criteria were as follows: 1) Intraoperative failure of the TIPS procedure. 2) Patients with a prior history of splenectomy and devascularization surgery (Hassab’s procedure). 3) Patients treated with non-selective beta blockers (NSBB). 4) Insufficient procedural, biochemical or radiological data. Prior to the interventional radiological procedure, all patient received a contrast CT examination of the hepatic portal system. Patients were required to fast for at least 4-hs prior the radiological examination. Intravenous contrast injection dye (Ultravist 370, Bayer Schering Pharma) at 3.0–3.5 ml/s was administered before obtaining transverse, sagittal and coronal images at 5 mm slice intervals with a MDCT (64slice Light Speed, GE Medical or 64-slice CT, Simens). As a routine examination, CT imaging is essential for pre-operative 3D reconstruction of the portal system, which allows for a clear depiction of the anatomical relationship between the hepatic vein and portal vein. Imaging studies can also evaluate concurrent conditions such as hepatocellular carcinoma (HCC), spontaneous portosystemic shunt (SPSS), ascites, or portal venous thrombosis (PVT), which are commonly observed in patients with portal hypertension [17]. 2.2. TIPS procedure The TIPS procedure was commenced after an overnight fast. The patient was placed in a supine position under electrocardiogram monitoring. The CT-based portosystemic 3D reconstruction was referenced 2
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Table 1 Baseline characteristics of the global population (n = 169). Global Population (n = 169) General Characteristics Gender Male Female Age (years) Child Pugh Score
104 (61.5 %) 65 (38.5 %) 56.63 ± 11.73 6.68 ± 1.36
Child Pugh Classification Class A Class B Class C
84 (49.7 %) 80 (47.3 %) 5 (3.0 %)
Clinical Presentation Variceal Hemorrhage Refractory Ascites Portal Venous Thrombosis Hypersplenism Mixed
135 (79.9 %) 18 (10.7 %) 5 (3.0 %) 2 (1.2 %) 9 (5.3 %)
Etiology of Portal Hypertension HBV HCV Alcohol PBC AIH Schistosomiasis Budd-Chiari Cryptogenic
84 (49.7 %) 8 (4.7 %) 7 (4.1 %) 6 (3.6 %) 9 (5.3 %) 9 (5.3 %) 9 (5.3 %) 37 (21.9 %)
Laboratory Parameters Total Bilirubin (μmol/L) Conjugated Bilirubin (μmol/L) Albumin (g/L) Globulin (g/L) ALT (U/L) AST (U/L) Hemoglobin (g/L) White Blood Cells (×109/L) Platelet (×109/L) Prothrombin Time (s) INR
19.24 ± 17.68 10.06 ± 13.47 34.72 ± 5.01 27.99 ± 6.48 24.83 ± 16.10 34.49 ± 24.09 89.65 ± 25.11 3.34 ± 2.15 79.85 ± 58.86 14.14 ± 1.78 1.31 ± 0.93
Intraoperative Measurements (mmHg) Portal Venous Pressure (PVP) Repeat PVP (n = 165) Difference in PVP (n = 165) Right Atrial Pressure (RAP) (n = 110) Repeat RAP (n = 139) Difference in RAP (n = 108)
28.82 ± 5.91 18.34 ± 4.58 10.29 ± 4.82 3.76 ± 2.04 7.22 ± 3.41 3.11 ± 2.82
Spleen Measurements Height (mm) Width (mm) Cranio-caudal Length (mm)
164.64 ± 36.57 58.19 ± 13.46 99.50 ± 31.49
Concurrent Conditions Portal Venous Thrombosis Absent Present
102 (60.4 %) 67 (39.6 %)
Hepatocellular Carcinoma Absent Present
145 (85.8 %) 24 (14.2 %)
Ascites Absent Mild Severe
80 (47.3 %) 78 (46.2 %) 11 (6.5 %)
Fig. 2. Manhattan plot of 1474 calculated P values. The Manhattan plot shows negative log value of all P values comparing the relationship between PVP values and each radiomic feature in the 169 subjects in the primary cohort. Features of seven categories were extracted from the liver and spleen ROI, namely F1 Gradient Orient Histogram, F2Gy-level Cooccurrence Matrix (GLCM), F3Gy-level Run-length Matrix (GLRLM), F4 Intensity Direct, F5 Intensity Histogram, F6 Neighbor Intensity Difference, and F7 Shape.
right hepatic vein or hepatic segment of the inferior vena cava was chosen for TIPS needle insertion to the branch of the portal vein (typically left branch). A complete anterior and lateral angiography of the portosystemic circulation was performed to ensure that the puncture site was secure. A 0.035 in Guide Wire M was used to deliver the 0.035in Amplatz super stiff wire (Cook Medical) into the splenic vein or superior mesenteric vein. A 4G Pigtail catheter (Cordis) was then placed at the main portal vein (1 cm proximal to the convergence point for splenic venous drainage) for direct PVP measurements. The 10F sheath was retracted to the posterior hepatic segment of the inferior vena cava for shunt tract angiography. After intravenous administration of 5000U heparin, an 8 mm × 40 mm balloon is used to dilate the shunt tract and an 8–10 mm × 60−100 mm Smart self-expanding stent (Cordis) or a nitinol self-expanding stent (Bard) was placed. A repeat PVP measurement was performed before embolizing the puncture tract with coils and gelatin sponge. An informed consent was signed by all study subjects, acknowledging the purpose and risks associated with the TIPS procedure. 2.3. Radiomics characteristics Radiological data of all included subjects were retrieved for radiomics features extraction. An open infrastructure software platform IBEX was used to extract both textural and non-textural radiomics features from the selected ROI for further analysis [18,19]. The original DICOM files were imported into IBEX, and the liver and spleen at the portal venous phase were manually traced as region of interest (ROI), with the porta hepatis and the splenic hilum as reference points, respectively (Fig. 1) [20,21]. 2.4. rPVP construction
for puncture guidance. Access to the left branch of the intrahepatic portal vein was achieved with a 21G Chiba needle (Cook Medical) under ultrasound guidance or direct puncture, and the catheter was left in place for marking. The right jugular vein was then accessed, and the
The least absolute shrinkage and selection operator (LASSO) method was used to identify the features with the highest predictability of PVP [22]. A rPVP formula was generated based on liver radiomics, 3
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Fig. 3. Heatmap and clustering of 1474 radiomic features. Each parameter is separately compared with all other features with linear regression and the corresponding R2 values are plotted as a heatmap. Higher correlation is indicated with increasingly darker colors.
as secondary prophylaxis. Each patient received appropriate individualized therapy. Gastric varices were treated with cyanoacrylate injection, while esophageal varices were treated with either endoscopic band ligation (EBL) or endoscopic injection sclerotherapy (EIS) as deemed fit by the operator.
spleen radiomics, and a combination of the two ROIs, via linear regression. 2.5. Validation cohort Patient data from a prospectively managed database were retrieved for the validation cohort. Patients admitted in our medical center for initial endoscopic treatment of gastroesophageal varices secondary to portal hypertension from May 2013 to May 2015 were eligible. Inclusion criteria were as follows: 1) Patient naïve to any form of treatment for gastroesophageal varices including oral medication, interventional radiology (TIPS or BRTO), or surgical therapy (splenectomy or Hassab’s procedure); 2) Patients who underwent CT imaging at our institute prior to endoscopic procedure; 3) Patients with available follow-up data, including repeat endoscopic examination for post-procedural assessment. Patients lost to follow-up, or those with incomplete radiological and clinical data were excluded from the validation cohort.
2.7. Follow-up Patients were closely followed at a designated outpatient service clinic, with detailed records of clinical data. All included subjects were divided into two groups based on their response to initial endoscopic treatment for gastroesophageal varices. Poor response was defined as report of variceal rebleeding, evident by melena or hematochezia, confirmed by a subsequent endoscopy examination. Patients with recurrent gastroesophageal varices with large varix or red-wales sign, judged as high-risk for hemorrhage, were characterized as having a poor response. Poor response to endoscopic therapy was an accurate reflection of disease severity, often associated with high portal pressure [24]. Good response was characterized by complete variceal obliteration that did not necessitate further intervention. Patient’s radiological data were retrieved for radiomics feature extraction as previously described. Based on a previously constructed formula, the rPVP for each patient were individually calculated. The correlation between rPVP and patient outcome was further examined.
2.6. Endoscopic treatment All endoscopic procedures were commenced after an overnight fast. First a routine endoscopic examination was performed for general assessment. Findings were classified according to Sarin’s classification, based on the extent and characteristics of gastroesophageal varices [23]. After thorough observation, endoscopic treatment was performed 4
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Fig. 4. Feature selection and cross-validation using LASSO. For different values of lambda, different numbers of features will be included in the final model and a decision has to be made. Figures A, C, E plot different values of lambda on the x-axis and each line represents a feature, showing when it enters the model and its level of influence on the outcome. Figures B, D, F illustrate the process of cross validation in order to choose the most appropriate lambda value. As shown on the figures, lambda resulting in the smallest mean cross-validated error is chosen. (A) Features of the LASSO penalty in the model containing liver features. (B) Cross-validation of the model containing liver features. (C) Features of the LASSO penalty in the model containing spleen features. (D) Cross-validation of the model containing spleen features. (E) Features of the LASSO penalty in the model containing both liver and spleen features. (F) Cross-validation of the model containing both liver and spleen features.
Fig. 5. Bland-Altman Plots. Bland-Altman plots provide comparison between rPVP and measured PVP for all three proposed models: rPVP (Liver), rPVP (Spleen), rPVP (Liver and Spleen).
2.8. Statistical analysis
3. Results
Statistical analyses were performed via SPSS 22 (IBM Corporation) and R Studio (Version 1.0.143). Categorical variables were presented as frequency (%), while continuous variables as mean ± standard deviation. Comparison between categorical variables was achieved through Pearson’s correlation, while continuous variables were compared using the independent Student’s t test. Feature selection was conducted using multivariate linear regression with the least absolute shrinkage and selection operator (LASSO) penalty. ROC curves and its respective indices were constructed to assess model efficacy in the validation cohort. Kaplan-Meier curves and log-rank tests were used to assess the time to variceal recurrence based on the optimal cut-off value. All statistical analyses were two-sided, and a p-value < 0.05 was considered statistically significant. The present study was approved by the Ethics Committee of Zhongshan Hospital, Fudan University (B2015133R).
3.1. Experiment cohort A total of 239 patients from October 2010 to October 2017 were admitted to a single tertiary medical center for the treatment of portal hypertension. A total of 69 patients were excluded based on prior exclusion criteria, including 27 with intraoperative failure due to complete occlusion of the main portal vein, which was subsequently converted to percutaneous transhepatic variceal embolization (PTVE), partial splenic embolization (PSE), or both, 1 case of arterio-porto venous fistula discovered during the procedure, which was converted to a transcatheter arterial embolization (TAE), 31 patients who were previously treated with splenectomy or Hassab’s procedure, 9 cases of insufficient operative note, biochemical parameters, or radiological data, and 2 patients who were receiving NSBB treatment. A final number of 169 patients were enrolled in the present study and their respective baseline characteristics are summarized in Table 1. Of the included subjects, 104 (61.5 %) patients were male, 65 (38.5 %) were female, with an average age of 56.63 ± 11.73 years old. Majority of the patients were classified as Child Pugh Class A and B (49.7 % and 47.3 %, respectively), with viral hepatitis (54.4 %) as the 5
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feature relationship was assessed with linear regression, depicted by a heatmap (Fig. 3). Details of feature extractions are listed in Appendix 1. The following rPVP formula was constructed via LASSO regression by selecting the most significant features that correlated with PVP (Fig. 4). Each feature is weighted by its respective coefficient. Three separate formulas of rPVP were constructed, based on radiomic features of the liver, spleen, and a combination of both ROIs. Bland-Altman plots were used to compare rPVP with measured PVP (Fig. 5).However, a systemic prediction error was noted, and was further adjusted with linear regression (Appendix 2). The slope of rPVP (liver), rPVP (spleen), and rPVP (Liver and Spleen) were 0.94, 0.77, and 0.58, respectively. This means that the predictive value (rPVP) tends to overestimate with every decrement in the value of PVP measured. The proposed rPVP formula are as follows (Appendix 1):
Table 2 Comparison between the good responders and poor responders of Validation Cohort.
General Characteristics Gender Male Female Age (years) Child Pugh Score
Good Responders (n = 29)
Poor Responders (n = 33)
p-value
18 (62.1 %) 11 (37.9 %) 59.62 ± 10.22 6.10 ± 1.18
19 (57.6 %) 14 (42.4 %) 55.55 ± 12.00 6.85 ± 1.72
0.719 0.158 0.054
Child Pugh Classification Class A 20 (69.0 %) Class B 9 (31.0 %) Class C 0 (0%)
17 (51.5 %) 14 (42.4 %) 2 (6.1 %)
0.214
Type of Gastroesophageal EV GOV Type 1 GOV Type 2 IGV Type 1 IGV Type 2
Varices 8 (27.6 %) 14 (48.3 %) 6 (20.7 %) 1 (3.4 %) 0 (0.0 %)
2 (6.1 %) 20 (60.6 %) 9 (27.3 %) 2 (6.1 %) 0 (0.0 %)
0.147
Etiology of Portal Hypertension HBV 15 (51.7 %) HCV 1 (3.4 %) Alcohol 3 (10.3 %) PBC 2 (6.9 %) AIH 1 (3.4 %) Schistosomiasis 1 (3.4 %) Cryptogenic 6 (20.7 %)
17 (51.5 %) 1 (3.0 %) 5 (15.2 %) 3 (9.1 %) 1 (3.0 %) 1 (3.0 %) 5 (15.2 %)
0.995
15.70 ± 7.0
25.92 ± 30.76
0.087
34.10 31.96 44.41 92.97 76.17 13.70
34.00 28.03 41.84 95.33 74.09 14.66
0.936 0.501 0.769 0.726 0.910 0.032
Laboratory Parameters Total Bilirubin (μmol/L) Albumin (g/L) ALT (U/L) AST (U/L) Hemoglobin (g/L) Platelet (×109/L) Prothrombin Time (s) INR
± ± ± ± ± ±
4.81 21.07 36.54 26.20 41.78 1.62
± ± ± ± ± ±
5.26 22.86 29.71 26.63 90.65 1.78
1.20 ± 0.9
1.26 ± 0.14
0.062
26.77 ± 2.14 26.79 ± 2.59 26.39 ± 2.45
30.09 ± 3.49 30.27 ± 2.71 30.72 ± 3.25
< 0.001 < 0.001 < 0.001
Concurrent Conditions Portal Venous Thrombosis Absent 21 (72.4 %) Present 8 (27.6 %)
26 (78.8 %) 7 (21.2 %)
0.559
Hepatocellular Carcinoma Absent 28 (96.6 %) Present 1 (3.4 %)
32 (97.0 %) 1 (3.0 %)
0.926
Ascites Absent Mild Severe
12 (36.4 %) 19 (57.6 %) 2 (6.1 %)
0.214
Radiomics derived PVP rPVP (Liver) rPVP (Spleen) rPVP (Liver and Spleen)
17 (58.6 %) 11 (37.9 %) 1 (3.4 %)
1 rPVP (Liver) = 0.083165(Shape Compactness1) + 0.009724(Shape Compactness2 + 0.00144(Intensity Direct – Global Min) – 1.745109(Gray Level Cooccurrence Matrix25 0–1 Correlation) – 12.882318(Intensity Direct Local entropy Std) + 0.334371(Gradient Orient Histogram – 10 Percentile) + 0.341511(Gradient Orient Histogram – Mean Absolute Deviation) – 0.852883(Intensity Direct – Local Entropy Max) – 2.276332(Gray Level Cooccurrence Matrix25 – 45-7 Correlation) – 0.444632(Gradient Orient Histogram – 15 Percentile) – 0.682513(Gradient Orient Histogram – 0.025 Quantile) + 0.047303 (Intensity Direct 0.025 Quantile) – 21.459645 2 rPVP (Spleen) = 5.067 – 0.06022(Shape Volume) + 0.05643S (Shape Mass) + 0.1403(Shape Compactness1) – 1.601e-10(Intensity Direct – Energy) – 2.895(Intensity Direct – Local Entropy Std) – 0.002501(Intensity Direct – Local Range Max) –0.00494 (Intensity Direct – Local Std Max) + 9.818(Gray Level Cooccurence Matrix25 0–4 Information Measure Corr2) + 19.35(Shape Convex) + 50.021(Gray Level Cooccurence Matrix25 – 0–4Information MeasureCorr1 + 38.99(Gray Level Run Length Matrix25 – 0 Gy Level Nonuniformity) 3 rPVP (Liver and Spleen) = 19.33 – 0.1589(S-Shape Volume) + 0.1447(S-Shape Mass) + 0.1225(S-Shape Compactness1) + 0.009046(L-Shape Compactness2) + 2.326e-10(S-Intensity Direct – Energy) – 1.213(S-Intensity Direct – Local Entropy Std) – 0.0007155 (S-Intensity Direct – Local Range Max) – 0.008068 (S-Intensity Direct – Local Std Max) + 12.79 (S-Gray Level Cooccurence Matrix25 0–4 Information Measure Corr2) + 19.37 (S-Shape Convex) + 64.67 (S-Gray Level Coocurence Matrix25 – 0–4 Information Measure Corr1) – 8.826 (L-Gray Level Cooccurence Matrix25 – 0–1 Correlation) – 3.438(L-Intensity Direct – Local Entropy Std) – 1.059 (L-Intensity Direct – Local Entropy Max). 3.3. Validation cohort Unlike HVPG, there is currently a lack of recognized standard for direct PVP, despite its unwavering accuracy. In order to evaluate the utility of rPVP, a validation cohort from a prospectively maintained database was referenced. A total of 62 patients were included in the validation cohort, with 37 (59.7 %) male patients, 25 (40.3 %) female patients, with an average age of 57.45 ± 11.30 years old. Majority of the patients were Child Class A and B (59.7 % and 37.1 %, respectively), and the most common etiology was viral hepatitis (54.8 %). All patients received initial endoscopic therapy for the treatment of gastroesophageal varices from May 2013 to May 2015, in which 10 patients received endoscopic band ligation (EBL), 1 patient received cyanoacrylate injection, and 51 patients received a combination of EBL plus cyanoacrylate injection. All patients were followed closely at a designated outpatient service clinic and received follow-up endoscopic examination to determine whether further endoscopic intervention was necessary. The average time to follow-up endoscopy was 324.31 ± 221.20 days. Based on patient reported clinical symptoms
most common etiology and variceal hemorrhage (79.9 %) as the most common clinical presentation. The average intraoperative portal venous pressure (PVP) was 28.82 ± 5.91 mmHg, which decreased by 10.29 ± 4.82 mmHg after shunt placement. 3.2. Radiomics characteristics The liver and the spleen ROIs were drawn at the level of the porta hepatis and splenic hilum, respectively (Fig. 1). A total of 738 features were extracted for each organ, under 7 feature categories (Fig. 2). Intra6
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Fig. 6. Boxplot. Boxplot representing rPVP of the validation cohort predicted based on the proposed formula, including features extracted from the liver, spleen or a combination of both ROI.
delineation of ROIs. The rPVP was calculated according to previously proposed formula and the results were as follows: rPVP (Liver) 28.54 ± 3.36 mmHg, rPVP (Spleen) 28.64 ± 3.16 mmHg, and rPVP (Liver and Spleen) 28.69 ± 3.16 mmHg (Fig. 6). The utility of rPVP was tested for its predictive value of patient response to endoscopic treatment for gastroesophageal varices. All three predicted rPVP were significantly correlated with patient response to initial endoscopic treatment (p < 0.001). The predictive capabilities of the proposed models were further tested with a receiver operator’s curve (ROC) (Fig. 7). The corresponding results of rPVP Liver, rPVP Spleen and rPVP Liver Spleen were 0.808, 0.843 and 0.866, respectively. Among which, rPVP (Liver and Spleen) has the highest predictability (Fig. 8), with an optimal cut-off value of 29.102 mmHg and a corresponding sensitivity of 73.5 % and specificity of 92.9 % (Table 3). A Kaplan Meier curve further demonstrated the difference between variceal recurrence based on the optimal cut-off value with reference to time (Log Rank MantelCox p < 0.001) (Fig. 9). 4. Discussion Liver cirrhosis is the most common cause of portal hypertension, which remains a global health burden, especially in developing countries [25]. Etiologies include viral hepatitis, alcohol, autoimmune hepatitis, steatohepatitis, and drug-induced liver disease [26]. Apart from sinusoidal etiologies, portal hypertension can result from pre-hepatic or post-hepatic diseases, such as Budd-Chiari or idiopathic portal hypertension [27,28]. Although HVPG is currently recognized as the standard approach for assessing portal hypertension, its accuracy may vary with a difference in etiology and present as falsely normal in nonsinusoidal portal hypertension. Other factors such as concurrent HCC or PVT may also affect HVPG measurements, causing a localized obstruction or change in portal pressure [29]. Theoretically, the direct measurement of portal pressure provides the most accurate and reliable reflection of disease severity. However, its invasive nature and associated high complication rates limits its application in clinical practice. Recent studies have explored other minimally invasive approaches to directly measure portal pressure, such as EUS-guided portal vein catherization [9,29]. However, this method has only been tested on porcine models and its safety and application on human patients warrants further clinical trials. The search for a noninvasive alternative for assessing portal pressure is unrelenting. For instance, the use of spleen
Fig. 7. ROC curve in the validation cohort in three models plotted in one figure. (A) ROC curve of the model containing liver features. (B) ROC curve of the model containing spleen features. (C) ROC curve of the model containing both liver and spleen features.
and repeat endoscopic examination, each subject was classified into good responders or poor responders. A total of 7 responders experienced hematochezia or melena, in whom variceal recurrence was confirmed by a subsequent endoscopy. Twenty-six patients, although asymptomatic, were diagnosed as having recurrent gastroesophageal varices with a high-risk of hemorrhage, including presence of large varix with or without red wales sign, necessitating further endoscopic intervention. A total of 33 patients fulfilled the definition of poor responders, while 29 patients achieved complete variceal obliteration without any clinical symptoms. Comparison between patient characteristics were summarized in Table 2. The CT images of all patients in the validation cohort were retrieved for radiomics features extraction. Similar to the experiment cohort, the portal hepatis and splenic hilum level was referenced for the 7
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Fig. 8. Performance of radiomic prediction. The relative rPVP of each patient in the validation cohort in each model is plotted as a bar plot. Patient outcomes are also indicated with different colors. (A) Radiomic prediction performance of the model containing liver features. (B) Radiomic prediction performance of the model containing spleen features. (C) Radiomic prediction performance of the model containing both liver and spleen features. Table 3 Model Accuracy based of Receiver Operating Characteristic (ROC) Curve. Radiomic Features
AUROC (95 % CI)
Threshold
Specificity
Sensitivity
Accuracy
NPV
PPV
Liver Spleen Liver Spleen
0.789 (0.676–0.90) 0.832 (0.723–0.941) 0.855 (0.760–0.951)
28.274 28.147 29.102
0.750 0.786 0.929
0.765 0.794 0.735
0.758 0.790 0.823
0.724 0.759 0.743
0.788 0.818 0.926
combination of both organs. Due to the lack of widespread practice for direct PVP measurement, there is currently no established diagnostic “gold” standard for PVP. Unlike HVPG, CSPH cannot be defined based on PVP values. Therefore, the clinical significance of PVP was further tested on a validation cohort which comprised of patients receiving endoscopic therapy for the treatment of gastroesophageal varices. All three predictive models (rPVP Liver, rPVP Spleen, rPVP Liver Spleen) exhibited satisfying precision. In comparison, rPVP Liver Spleen yielded the highest AUROC of 0.866 and optimal cut-off value of 29.102 mmHg, resulting in a more stringent model, hence the higher specificity and lower sensitivity (Table 3). Patients with high predicted rPVP may not benefit from the traditional endoscopic treatment for gastroesophageal varices. Instead, other treatment options, such as TIPS or surgery, should be sought. rPVP can serve as a useful reference for physicians in recommending an appropriate personalized therapy for the treatment of gastroesophageal varices. Based on the preliminary results, we incorporated radiomics in the diagnosis, outcome prediction and treatment selection for patients with portal hypertension. To the best of our knowledge, this study is the first to report the use of radiomics for the assessment of portal hypertension. However, there are several limitations that needs to be addressed. The lack of direct PVP measurement in the validation cohort crippled the evidential strength of the proposed model. The small sample size of both the experimental and validation cohort may limit the interpretation of results. The validation cohort only comprised of patients with gastroesophageal varices and predicted treatment response to endoscopic treatment. Therefore, application of the results in other symptoms of decompensation such as refractory ascites or hepatic encephalopathy, should be carried out with caution. The use of clinical imaging has been rapidly evolving of the past decade. Radiomics employs available tomographic studies and extracts innumerable quantitative features into mineable high-dimensional data. Apart from providing visual diagnostic information, tomographic images have characteristics that are often neglected by the naked eye. With the rise of machine learning and artificial intelligence in the
Fig. 9. Kaplan-Meier Curve. Based on the optimal cut-off value of 29.102 mmHg, a KM curve demonstrated the difference between variceal recurrence and nonrecurrence after initial endoscopic therapy for the treatment of gastroesophageal varices. Log Rank Mantel-Cox (p < 0.001).
and liver stiffness in predicting HVPG showed promising accuracy [21]. Other studies have also demonstrated the correlation between CT perfusion and HVPG [30]. However, its utility in reflecting actual PVP and disease severity remains precarious, especially in non-sinusoidal etiologies of portal hypertension. The present study proposed a non-invasive model in predicting portal venous pressure through an image-derived algorithm. Radiomic features were extracted from both liver and spleen and analyzed on an open infrastructure software platform: IBEX [18,19]. Features with the highest predictability were filtered from over hundreds of features via the LASSO method to construct a model for radiomics-derived portal venous pressure (rPVP). Three rPVP predictive models were constructed based on features derived from the liver, spleen and a 8
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medical industry, disease diagnosis and management can be revolutionized [13,16]. The general idea of machine learning lies within the ability to predict outcomes from measurable predictors. In this study, we were able to predict PVP values via class prediction. Direct PVP measurement is clinically inconvenient to obtain, but can be derived from the easily accessible CT studies with promising accuracy. Due to the randomness included in the statistical process, such as crossvalidation, a slight difference in prediction might occur given the same set of features in class prediction. This study provides insight to the many possible applications of machine learning, which is a process of continuous optimization that can potentially revolutionize the medical industry.
1002/hep.510300628. [9] S.A. Giday, J.O. Clarke, J.M. Buscaglia, E.J. Shin, C.W. Ko, P. Magno, et al., EUSguided portal vein catheterization: a promising novel approach for portal angiography and portal vein pressure measurements, Gastrointest. Endosc. 67 (2008) 338–342, https://doi.org/10.1016/j.gie.2007.08.037. [10] S.K. Sarin, K.K. Sethi, R. Nanda, Measurement and correlation of wedged hepatic, intrahepatic, intrasplenic and intravariceal pressures in patients with cirrhosis of liver and non-cirrhotic portal fibrosis, Gut 28 (1987) 260–266. [11] G. Maleux, E. Willems, S. Fieuws, S. Heye, J. Vaninbroukx, W. Laleman, et al., Prospective study comparing different indirect methods to measure portal pressure, J. Vasc. Interv. Radiol. 22 (2011) 1553–1558, https://doi.org/10.1016/j.jvir. 2011.08.003. [12] R.J. Gillies, P.E. Kinahan, H. Hricak, Radiomics: images are more than pictures, they are data, Radiology 278 (2016) 563–577, https://doi.org/10.1148/radiol. 2015151169. [13] P. Lambin, R. Leijenaar, T.M. Deist, J. Peerlings, E. de Jong, J. van Timmeren, et al., Radiomics: the bridge between medical imaging and personalized medicine, Nat. Rev. Clin. Oncol. 14 (2017) 749–762, https://doi.org/10.1038/nrclinonc.2017. 141. [14] Z. Liu, X.Y. Zhang, Y.J. Shi, L. Wang, H.T. Zhu, Z. Tang, et al., Radiomics analysis for evaluation of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer, Clin. Cancer Res. 23 (2017) 7253–7262, https://doi.org/10.1158/1078-0432.CCR-17-1038. [15] Y.Q. Huang, C.H. Liang, L. He, J. Tian, C.S. Liang, X. Chen, et al., Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer, J. Clin. Oncol. 34 (2016) 2157–2164, https://doi. org/10.1200/JCO.2015.65.9128. [16] D.S. Kermany, M. Goldbaum, W. Cai, et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell 172 (2018) 1122–1131 e9. [17] T. Murakami, K. Mochizuki, H. Nakamura, Imaging evaluation of the cirrhotic liver, Semin. Liver Dis. 21 (2001) 213–224, https://doi.org/10.1055/s-2001-15497. [18] L. Zhang, D.V. Fried, X.J. Fave, L.A. Hunter, J. Yang, L.E. Court, IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics, Med. Phys. 42 (2015) 1341–1353, https://doi.org/10.1118/1.4908210. [19] R.B. Ger, C.E. Cardenas, B.M. Anderson, et al., Guidelines and experience using imaging biomarker explorer (IBEX) for radiomics, J. Visualized Exp. (Jan. (131)) (2018), https://doi.org/10.3791/57132. [20] P. Iranmanesh, O. Vazquez, S. Terraz, P. Majno, L. Spahr, A. Poncet, et al., Accurate computed tomography-based portal pressure assessment in patients with hepatocellular carcinoma, J. Hepatol. 60 (2014) 969–974, https://doi.org/10.1016/j.jhep. 2013.12.015. [21] A. Colecchia, L. Montrone, E. Scaioli, M.L. Bacchi-Reggiani, A. Colli, G. Casazza, et al., Measurement of spleen stiffness to evaluate portal hypertension and the presence of esophageal varices in patients with HCV-related cirrhosis, Gastroenterology 143 (2012) 646–654, https://doi.org/10.1053/j.gastro.2012.05. 035. [22] W. Sauerbrei, P. Royston, H. Binder, Selection of important variables and determination of functional form for continuous predictors in multivariable model building, Stat. Med. 26 (2007) 5512–5528, https://doi.org/10.1002/sim.3148. [23] S.K. Sarin, D. Lahoti, S.P. Saxena, N.S. Murthy, U.K. Makwana, Prevalence, classification and natural history of gastric varices: a long-term follow-up study in 568 portal hypertension patients, Hepatology 16 (1992) 1343–1349. [24] Y. Idezuki, General rules for recording endoscopic findings of esophagogastric varices (1991). Japanese Society for Portal Hypertension, World J. Surg. 19 (1995) 420–422 423. [25] A.A. Mokdad, A.D. Lopez, S. Shahraz, R. Lozano, A.H. Mokdad, J. Stanaway, et al., Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis, BMC Med. 12 (2014) 145, https://doi.org/10.1186/s12916-014-0145-y. [26] C. Estes, H. Razavi, R. Loomba, et al., Modeling the epidemic of nonalcoholic fatty liver disease demonstrates an exponential increase in burden of disease, Hepatology 67 (2018) 123–133. [27] S. Hillaire, E. Bonte, M.H. Denninger, N. Casadevall, J.F. Cadranel, D. Lebrec, et al., Idiopathic non-cirrhotic intrahepatic portal hypertension in the West: a re-evaluation in 28 patients, Gut 51 (2002) 275–280. [28] S.K. Sarin, A. Kumar, P.W. Angus, S.S. Baijal, S.K. Baik, Y. Bayraktar, et al., Diagnosis and management of acute variceal bleeding: Asian Pacific Association for Study of the Liver recommendations, Hepatol. Int. 5 (2011) 607–624, https://doi. org/10.1007/s12072-010-9236-9. [29] A.R. Schulman, C.C. Thompson, M. Ryou, EUS-guided portal pressure measurement using a digital pressure wire with real-time remote display: a novel, minimally invasive technique for direct measurement in an animal model, Gastrointest. Endosc. 83 (2016) 817–820, https://doi.org/10.1016/j.gie.2015.11.038. [30] E. Talakić, S. Schaffellner, D. Kniepeiss, H. Mueller, R. Stauber, F. Quehenberger, et al., CT perfusion imaging of the liver and the spleen in patients with cirrhosis: Is there a correlation between perfusion and portal venous hypertension? Eur. Radiol. 27 (2017) 4173–4180, https://doi.org/10.1007/s00330-017-4788-x.
CRediT authorship contribution statement Yujen Tseng: Data curation, Writing - original draft. Lili Ma: Formal analysis, Writing - original draft. Shaobo Li: Formal analysis, Methodology. Tiancheng Luo: Formal analysis, Project administration. Jianjun Luo: Investigation, Resources. Wen Zhang: Investigation, Resources. Jian Wang: Conceptualization, Funding acquisition. Shiyao Chen: Supervision. Declaration of Competing Interest All authors declare no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. Acknowledgement This study was supported by the Innovation Fund of Shanghai Scientific Committee (No. 15411950501). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ejrad.2020.108927. References [1] G. Garcia-Tsao, J.K. Lim, Management and treatment of patients with cirrhosis and portal hypertension: recommendations from the Department of Veterans Affairs Hepatitis C Resource Center Program and the National Hepatitis C Program, Am. J. Gastroenterol. 104 (2009) 1802–1829, https://doi.org/10.1038/ajg.2009.191. [2] R. de Franchis, Expanding consensus in portal hypertension: report of the Baveno VI Consensus Workshop: stratifying risk and individualizing care for portal hypertension, J. Hepatol. 63 (2015) 743–752, https://doi.org/10.1016/j.jhep.2015. 05.022. [3] G. Garcia-Tsao, A.J. Sanyal, N.D. Grace, W. Carey, Prevention and management of gastroesophageal varices and variceal hemorrhage in cirrhosis, Hepatology 46 (2007) 922–938, https://doi.org/10.1002/hep.21907. [6] J. Bosch, J.G. Abraldes, A. Berzigotti, J.C. Garcia-Pagan, The clinical use of HVPG measurements in chronic liver disease, Nat. Rev. Gastroenterol. Hepatol. 6 (2009) 573–582, https://doi.org/10.1038/nrgastro.2009.149. [7] S. Keiding, J. Solvig, H. Gronbaek, H. Vilstrup, Combined liver vein and spleen pulp pressure measurements in patients with portal or splenic vein thrombosis, Scand. J. Gastroenterol. 39 (2004) 594–599, https://doi.org/10.1080/00365520410005171. [8] A. Perello, A. Escorsell, C. Bru, R. Gilabert, E. Moitinho, J.C. Garcia-Pagan, et al., Wedged hepatic venous pressure adequately reflects portal pressure in hepatitis C virus-related cirrhosis, Hepatology 30 (1999) 1393–1397, https://doi.org/10.
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European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Research article
Application of CT-based radiomics in predicting portal pressure and patient outcome in portal hypertension
T
Yujen Tsenga,b,1, Lili Mac,1, Shaobo Lid,1, Tiancheng Luoa,1, Jianjun Luoe, Wen Zhange, Jian Wanga, Shiyao Chena,b,f,* a
Department of Gastroenterology,Zhongshan Hosptial, Fudan University, China Department of Digestive Diseases, Huashan Hospital, Fudan University, China c Department of Endoscopy Center, Zhongshan Hospital, Fudan University, China d Shanghai Medical College, Fudan University, China e Department of Interventional Radiology, Zhongshan Hospital, Fudan University, China f Evidence-Based Medicine Center, Zhongshan Hospital, Fudan University, China b
A R T I C LE I N FO
A B S T R A C T
Keywords: Portal venous pressure Radiomics Esophageal and gastric varices Portal hypertension
Purpose: Portal venous pressure (PVP) measurement is of clinical significance, especially in patients with portal hypertension. However, the invasive nature and associated complications limits its application. The aim of the study is to propose a noninvasive predictive model of PVP values based on CT-extracted radiomic features. Methods: Radiomics PVP (rPVP) models based on liver, spleen and combined features were established on an experimental cohort of 169 subjects. Radiomics features were extracted from each ROI and reduced via the LASSO regression to achieve an optimal predictive formula. A validation cohort of 62 patients treated for gastroesophageal varices (GOV) was used to confirm the utility of rPVP in predicting variceal recurrence. The association between rPVP and response to treatment was observed. Results: Three separate predictive formula for PVP were derived from radiomics features. rPVP was significantly correlated to patient response to endoscopic treatment for GOV. Among which, the model containing both liver and spleen features has the highest predictability of variceal recurrence, with an optimal cut-off value at 29.102 mmHg (AUC 0.866). A Kaplan Meier analysis further confirmed the difference between patients with varying rPVP values. Conclusion: PVP values can be accurately predicted by a non-invasive, CT derived radiomics model. rPVP serves as a non-invasive and precise reference for predicting treatment outcome for GOV secondary to portal hypertension.
1. Introduction Portal hypertension is associated with the most severe complications of liver cirrhosis, including ascites, hepatic encephalopathy and gastroesophageal varices [1]. Among which, gastroesophageal variceal bleeding is considered the most fatal consequence of portal
hypertension. Despite recent advancements in treatment, gastroesophageal variceal bleeding is still associated with a high mortality rate of 10–20 % at 6 weeks [2,3]. According to the Baveno VI Consensus, first line therapy for gastroesophageal varices includes a combination of endoscopic band ligation (EBL), cyanoacrylate injection, and non-selective beta blockers (propranolol or nadolol) [2]. Although
Abbreviations: PVP, portal venous pressure; CT, computer tomography; rPVP, radiomics portal venous pressure; GOV, gastroesophageal varices; AUC, area under curve; KM, Kaplan Meier; EBL, endoscopic band ligation; HVPG, hepatic venous pressure gradient; CSPH, clinically significant portal hypertension; WHVPG, wedge hepatic venous pressure; FHVPG, free hepatic venous pressure; MRI, magnetic resonance imaging; PET, positron emission tomography; NSBB, non-selective beta blockers; TIPS, transjugular intrahepatic portosystemic shunt; HCC, hepatocellular carcinoma; SPSS, spontaneous portosystemic shunt; PVT, portal venous thrombosis; ROI, region of interest; BRTO, balloon-assisted retrograde transvenous obliteration; EIS, endoscopic injection sclerotherapy; PTVE, percutaneous transhepatic variceal embolization; PSE, partial splenic embolization; TAE, transcatheter arterial embolization; MDCT, multi-detector row computer tomography ⁎ Corresponding author at: Department of Gastroenterology, Endoscopy Center, Evidence-Based Medicine Center, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 0086200032, China. E-mail address: [email protected] (S. Chen). 1 Authors contributed equally to the manuscript and share first authorship. https://doi.org/10.1016/j.ejrad.2020.108927 Received 4 September 2019; Received in revised form 25 February 2020; Accepted 28 February 2020 0720-048X/ © 2020 Elsevier B.V. All rights reserved.
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Fig. 1. Selection of Region of Interest (ROI). Delineation of the liver and spleen as independent ROI in the IBEX software for extraction of radiomic features.
measurements achieved during the TIPS procedure. However, due to the current lack of diagnostic reference for direct PVP, the proposed model is further validated in a group of patients with gastroesophageal varices, to confirm its clinical value in predicting gastroesophageal variceal recurrence. In this study, we were able to predict PVP values via class prediction. Direct PVP measurement is clinically inconvenient to obtain, but can be derived from the easily accessible CT studies with promising accuracy.
repeated endotherapy sessions are widely accepted and practiced, some patients fail to achieve complete variceal obliteration, necessitating an eventual change in course of treatment [2]. Hepatic venous pressure gradient (HVPG) is currently recognized as the “gold-standard” for assessing portal hypertension, which is used to diagnose clinically significant portal hypertension (CSPH, defined as HVPG ≥10 mmHg) and provide prognostic information. Based on the research agenda recommended by the Baveno VI Consensus, portal pressure can also be referenced for therapeutic guidance [2]. However, the invasive nature and high costs of HVPG limits its clinical application as a surveillance modality. HVPG is a surrogate outcome reflecting the difference between wedge hepatic venous pressure (WHVP) and free hepatic venous pressure (FHVP) [6]. The indirect portal venous pressure (PVP) measurement reflected via HVPG may be an inadequate estimate of the actual PVP, hence, disease severity [7–10]. Direct measurement of portal pressure has been previously reported and can be achieved through a direct puncture of the portal vein, either through a percutaneous or transjugular approach. However, the direct approach is associated with a higher risk of complication including hepatic hemorrhage, portal vein thrombosis, and intrahepatic arteriovenous fistula [8,11]. Over the past decade, advancements in medical imaging analysis has grown exponentially. Radiomics is the extraction of quantitative features of tomographic images (CT, MRI, PET) into mineable data and the exploration of their significance to support clinical decision making. Application of high-throughput computing allows for the rapid extraction of innumerable high-dimensional data and pattern recognition. The application of radiomics is boundless, from increasing diagnostic precision, to assessment of prognosis and therapy response [12,13]. Many researchers have explored the utility of radiomics in field of oncology [14,15], such as lesion characterization and prediction of treatment response or metastasis. The utility of radiomics in other clinical specialties warrants further investigation. Radiomics employs available tomographic studies and extracts innumerable quantitative features into mineable high-dimensional data. Apart from providing visual diagnostic information, tomographic images have characteristics that are often neglected by the naked eye. With the rise of machine learning and artificial intelligence in the medical industry, disease diagnosis and management can be revolutionized [13,16]. The general idea of machine learning lies in the ability to predict outcomes from measurable predictors. The present study is aimed at exploring the application of radiomics in liver disease by proposing a noninvasive model for diagnosing PVP in patients with portal hypertension. An experimental cohort was used to construct a PVP prediction model based on CT-derived radiomics and direct PVP
2. Materials and methods 2.1. Experiment cohort Patients admitted to a tertiary medical center from October 2010 to October 2017 for a transjugular intrahepatic portosystemic shunt (TIPS) procedure were eligible for study participation. Inclusion criteria were as follows: 1) Patient diagnosed with portal hypertension, evident by recurrent gastroesophageal variceal hemorrhage, refractory ascites, portal venous thrombosis, hypersplenism, or a combination of symptoms. 2) Patient with available intraoperative direct measurement of portal venous pressure (PVP). Exclusion criteria were as follows: 1) Intraoperative failure of the TIPS procedure. 2) Patients with a prior history of splenectomy and devascularization surgery (Hassab’s procedure). 3) Patients treated with non-selective beta blockers (NSBB). 4) Insufficient procedural, biochemical or radiological data. Prior to the interventional radiological procedure, all patient received a contrast CT examination of the hepatic portal system. Patients were required to fast for at least 4-hs prior the radiological examination. Intravenous contrast injection dye (Ultravist 370, Bayer Schering Pharma) at 3.0–3.5 ml/s was administered before obtaining transverse, sagittal and coronal images at 5 mm slice intervals with a MDCT (64slice Light Speed, GE Medical or 64-slice CT, Simens). As a routine examination, CT imaging is essential for pre-operative 3D reconstruction of the portal system, which allows for a clear depiction of the anatomical relationship between the hepatic vein and portal vein. Imaging studies can also evaluate concurrent conditions such as hepatocellular carcinoma (HCC), spontaneous portosystemic shunt (SPSS), ascites, or portal venous thrombosis (PVT), which are commonly observed in patients with portal hypertension [17]. 2.2. TIPS procedure The TIPS procedure was commenced after an overnight fast. The patient was placed in a supine position under electrocardiogram monitoring. The CT-based portosystemic 3D reconstruction was referenced 2
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Table 1 Baseline characteristics of the global population (n = 169). Global Population (n = 169) General Characteristics Gender Male Female Age (years) Child Pugh Score
104 (61.5 %) 65 (38.5 %) 56.63 ± 11.73 6.68 ± 1.36
Child Pugh Classification Class A Class B Class C
84 (49.7 %) 80 (47.3 %) 5 (3.0 %)
Clinical Presentation Variceal Hemorrhage Refractory Ascites Portal Venous Thrombosis Hypersplenism Mixed
135 (79.9 %) 18 (10.7 %) 5 (3.0 %) 2 (1.2 %) 9 (5.3 %)
Etiology of Portal Hypertension HBV HCV Alcohol PBC AIH Schistosomiasis Budd-Chiari Cryptogenic
84 (49.7 %) 8 (4.7 %) 7 (4.1 %) 6 (3.6 %) 9 (5.3 %) 9 (5.3 %) 9 (5.3 %) 37 (21.9 %)
Laboratory Parameters Total Bilirubin (μmol/L) Conjugated Bilirubin (μmol/L) Albumin (g/L) Globulin (g/L) ALT (U/L) AST (U/L) Hemoglobin (g/L) White Blood Cells (×109/L) Platelet (×109/L) Prothrombin Time (s) INR
19.24 ± 17.68 10.06 ± 13.47 34.72 ± 5.01 27.99 ± 6.48 24.83 ± 16.10 34.49 ± 24.09 89.65 ± 25.11 3.34 ± 2.15 79.85 ± 58.86 14.14 ± 1.78 1.31 ± 0.93
Intraoperative Measurements (mmHg) Portal Venous Pressure (PVP) Repeat PVP (n = 165) Difference in PVP (n = 165) Right Atrial Pressure (RAP) (n = 110) Repeat RAP (n = 139) Difference in RAP (n = 108)
28.82 ± 5.91 18.34 ± 4.58 10.29 ± 4.82 3.76 ± 2.04 7.22 ± 3.41 3.11 ± 2.82
Spleen Measurements Height (mm) Width (mm) Cranio-caudal Length (mm)
164.64 ± 36.57 58.19 ± 13.46 99.50 ± 31.49
Concurrent Conditions Portal Venous Thrombosis Absent Present
102 (60.4 %) 67 (39.6 %)
Hepatocellular Carcinoma Absent Present
145 (85.8 %) 24 (14.2 %)
Ascites Absent Mild Severe
80 (47.3 %) 78 (46.2 %) 11 (6.5 %)
Fig. 2. Manhattan plot of 1474 calculated P values. The Manhattan plot shows negative log value of all P values comparing the relationship between PVP values and each radiomic feature in the 169 subjects in the primary cohort. Features of seven categories were extracted from the liver and spleen ROI, namely F1 Gradient Orient Histogram, F2Gy-level Cooccurrence Matrix (GLCM), F3Gy-level Run-length Matrix (GLRLM), F4 Intensity Direct, F5 Intensity Histogram, F6 Neighbor Intensity Difference, and F7 Shape.
right hepatic vein or hepatic segment of the inferior vena cava was chosen for TIPS needle insertion to the branch of the portal vein (typically left branch). A complete anterior and lateral angiography of the portosystemic circulation was performed to ensure that the puncture site was secure. A 0.035 in Guide Wire M was used to deliver the 0.035in Amplatz super stiff wire (Cook Medical) into the splenic vein or superior mesenteric vein. A 4G Pigtail catheter (Cordis) was then placed at the main portal vein (1 cm proximal to the convergence point for splenic venous drainage) for direct PVP measurements. The 10F sheath was retracted to the posterior hepatic segment of the inferior vena cava for shunt tract angiography. After intravenous administration of 5000U heparin, an 8 mm × 40 mm balloon is used to dilate the shunt tract and an 8–10 mm × 60−100 mm Smart self-expanding stent (Cordis) or a nitinol self-expanding stent (Bard) was placed. A repeat PVP measurement was performed before embolizing the puncture tract with coils and gelatin sponge. An informed consent was signed by all study subjects, acknowledging the purpose and risks associated with the TIPS procedure. 2.3. Radiomics characteristics Radiological data of all included subjects were retrieved for radiomics features extraction. An open infrastructure software platform IBEX was used to extract both textural and non-textural radiomics features from the selected ROI for further analysis [18,19]. The original DICOM files were imported into IBEX, and the liver and spleen at the portal venous phase were manually traced as region of interest (ROI), with the porta hepatis and the splenic hilum as reference points, respectively (Fig. 1) [20,21]. 2.4. rPVP construction
for puncture guidance. Access to the left branch of the intrahepatic portal vein was achieved with a 21G Chiba needle (Cook Medical) under ultrasound guidance or direct puncture, and the catheter was left in place for marking. The right jugular vein was then accessed, and the
The least absolute shrinkage and selection operator (LASSO) method was used to identify the features with the highest predictability of PVP [22]. A rPVP formula was generated based on liver radiomics, 3
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Fig. 3. Heatmap and clustering of 1474 radiomic features. Each parameter is separately compared with all other features with linear regression and the corresponding R2 values are plotted as a heatmap. Higher correlation is indicated with increasingly darker colors.
as secondary prophylaxis. Each patient received appropriate individualized therapy. Gastric varices were treated with cyanoacrylate injection, while esophageal varices were treated with either endoscopic band ligation (EBL) or endoscopic injection sclerotherapy (EIS) as deemed fit by the operator.
spleen radiomics, and a combination of the two ROIs, via linear regression. 2.5. Validation cohort Patient data from a prospectively managed database were retrieved for the validation cohort. Patients admitted in our medical center for initial endoscopic treatment of gastroesophageal varices secondary to portal hypertension from May 2013 to May 2015 were eligible. Inclusion criteria were as follows: 1) Patient naïve to any form of treatment for gastroesophageal varices including oral medication, interventional radiology (TIPS or BRTO), or surgical therapy (splenectomy or Hassab’s procedure); 2) Patients who underwent CT imaging at our institute prior to endoscopic procedure; 3) Patients with available follow-up data, including repeat endoscopic examination for post-procedural assessment. Patients lost to follow-up, or those with incomplete radiological and clinical data were excluded from the validation cohort.
2.7. Follow-up Patients were closely followed at a designated outpatient service clinic, with detailed records of clinical data. All included subjects were divided into two groups based on their response to initial endoscopic treatment for gastroesophageal varices. Poor response was defined as report of variceal rebleeding, evident by melena or hematochezia, confirmed by a subsequent endoscopy examination. Patients with recurrent gastroesophageal varices with large varix or red-wales sign, judged as high-risk for hemorrhage, were characterized as having a poor response. Poor response to endoscopic therapy was an accurate reflection of disease severity, often associated with high portal pressure [24]. Good response was characterized by complete variceal obliteration that did not necessitate further intervention. Patient’s radiological data were retrieved for radiomics feature extraction as previously described. Based on a previously constructed formula, the rPVP for each patient were individually calculated. The correlation between rPVP and patient outcome was further examined.
2.6. Endoscopic treatment All endoscopic procedures were commenced after an overnight fast. First a routine endoscopic examination was performed for general assessment. Findings were classified according to Sarin’s classification, based on the extent and characteristics of gastroesophageal varices [23]. After thorough observation, endoscopic treatment was performed 4
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Fig. 4. Feature selection and cross-validation using LASSO. For different values of lambda, different numbers of features will be included in the final model and a decision has to be made. Figures A, C, E plot different values of lambda on the x-axis and each line represents a feature, showing when it enters the model and its level of influence on the outcome. Figures B, D, F illustrate the process of cross validation in order to choose the most appropriate lambda value. As shown on the figures, lambda resulting in the smallest mean cross-validated error is chosen. (A) Features of the LASSO penalty in the model containing liver features. (B) Cross-validation of the model containing liver features. (C) Features of the LASSO penalty in the model containing spleen features. (D) Cross-validation of the model containing spleen features. (E) Features of the LASSO penalty in the model containing both liver and spleen features. (F) Cross-validation of the model containing both liver and spleen features.
Fig. 5. Bland-Altman Plots. Bland-Altman plots provide comparison between rPVP and measured PVP for all three proposed models: rPVP (Liver), rPVP (Spleen), rPVP (Liver and Spleen).
2.8. Statistical analysis
3. Results
Statistical analyses were performed via SPSS 22 (IBM Corporation) and R Studio (Version 1.0.143). Categorical variables were presented as frequency (%), while continuous variables as mean ± standard deviation. Comparison between categorical variables was achieved through Pearson’s correlation, while continuous variables were compared using the independent Student’s t test. Feature selection was conducted using multivariate linear regression with the least absolute shrinkage and selection operator (LASSO) penalty. ROC curves and its respective indices were constructed to assess model efficacy in the validation cohort. Kaplan-Meier curves and log-rank tests were used to assess the time to variceal recurrence based on the optimal cut-off value. All statistical analyses were two-sided, and a p-value < 0.05 was considered statistically significant. The present study was approved by the Ethics Committee of Zhongshan Hospital, Fudan University (B2015133R).
3.1. Experiment cohort A total of 239 patients from October 2010 to October 2017 were admitted to a single tertiary medical center for the treatment of portal hypertension. A total of 69 patients were excluded based on prior exclusion criteria, including 27 with intraoperative failure due to complete occlusion of the main portal vein, which was subsequently converted to percutaneous transhepatic variceal embolization (PTVE), partial splenic embolization (PSE), or both, 1 case of arterio-porto venous fistula discovered during the procedure, which was converted to a transcatheter arterial embolization (TAE), 31 patients who were previously treated with splenectomy or Hassab’s procedure, 9 cases of insufficient operative note, biochemical parameters, or radiological data, and 2 patients who were receiving NSBB treatment. A final number of 169 patients were enrolled in the present study and their respective baseline characteristics are summarized in Table 1. Of the included subjects, 104 (61.5 %) patients were male, 65 (38.5 %) were female, with an average age of 56.63 ± 11.73 years old. Majority of the patients were classified as Child Pugh Class A and B (49.7 % and 47.3 %, respectively), with viral hepatitis (54.4 %) as the 5
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feature relationship was assessed with linear regression, depicted by a heatmap (Fig. 3). Details of feature extractions are listed in Appendix 1. The following rPVP formula was constructed via LASSO regression by selecting the most significant features that correlated with PVP (Fig. 4). Each feature is weighted by its respective coefficient. Three separate formulas of rPVP were constructed, based on radiomic features of the liver, spleen, and a combination of both ROIs. Bland-Altman plots were used to compare rPVP with measured PVP (Fig. 5).However, a systemic prediction error was noted, and was further adjusted with linear regression (Appendix 2). The slope of rPVP (liver), rPVP (spleen), and rPVP (Liver and Spleen) were 0.94, 0.77, and 0.58, respectively. This means that the predictive value (rPVP) tends to overestimate with every decrement in the value of PVP measured. The proposed rPVP formula are as follows (Appendix 1):
Table 2 Comparison between the good responders and poor responders of Validation Cohort.
General Characteristics Gender Male Female Age (years) Child Pugh Score
Good Responders (n = 29)
Poor Responders (n = 33)
p-value
18 (62.1 %) 11 (37.9 %) 59.62 ± 10.22 6.10 ± 1.18
19 (57.6 %) 14 (42.4 %) 55.55 ± 12.00 6.85 ± 1.72
0.719 0.158 0.054
Child Pugh Classification Class A 20 (69.0 %) Class B 9 (31.0 %) Class C 0 (0%)
17 (51.5 %) 14 (42.4 %) 2 (6.1 %)
0.214
Type of Gastroesophageal EV GOV Type 1 GOV Type 2 IGV Type 1 IGV Type 2
Varices 8 (27.6 %) 14 (48.3 %) 6 (20.7 %) 1 (3.4 %) 0 (0.0 %)
2 (6.1 %) 20 (60.6 %) 9 (27.3 %) 2 (6.1 %) 0 (0.0 %)
0.147
Etiology of Portal Hypertension HBV 15 (51.7 %) HCV 1 (3.4 %) Alcohol 3 (10.3 %) PBC 2 (6.9 %) AIH 1 (3.4 %) Schistosomiasis 1 (3.4 %) Cryptogenic 6 (20.7 %)
17 (51.5 %) 1 (3.0 %) 5 (15.2 %) 3 (9.1 %) 1 (3.0 %) 1 (3.0 %) 5 (15.2 %)
0.995
15.70 ± 7.0
25.92 ± 30.76
0.087
34.10 31.96 44.41 92.97 76.17 13.70
34.00 28.03 41.84 95.33 74.09 14.66
0.936 0.501 0.769 0.726 0.910 0.032
Laboratory Parameters Total Bilirubin (μmol/L) Albumin (g/L) ALT (U/L) AST (U/L) Hemoglobin (g/L) Platelet (×109/L) Prothrombin Time (s) INR
± ± ± ± ± ±
4.81 21.07 36.54 26.20 41.78 1.62
± ± ± ± ± ±
5.26 22.86 29.71 26.63 90.65 1.78
1.20 ± 0.9
1.26 ± 0.14
0.062
26.77 ± 2.14 26.79 ± 2.59 26.39 ± 2.45
30.09 ± 3.49 30.27 ± 2.71 30.72 ± 3.25
< 0.001 < 0.001 < 0.001
Concurrent Conditions Portal Venous Thrombosis Absent 21 (72.4 %) Present 8 (27.6 %)
26 (78.8 %) 7 (21.2 %)
0.559
Hepatocellular Carcinoma Absent 28 (96.6 %) Present 1 (3.4 %)
32 (97.0 %) 1 (3.0 %)
0.926
Ascites Absent Mild Severe
12 (36.4 %) 19 (57.6 %) 2 (6.1 %)
0.214
Radiomics derived PVP rPVP (Liver) rPVP (Spleen) rPVP (Liver and Spleen)
17 (58.6 %) 11 (37.9 %) 1 (3.4 %)
1 rPVP (Liver) = 0.083165(Shape Compactness1) + 0.009724(Shape Compactness2 + 0.00144(Intensity Direct – Global Min) – 1.745109(Gray Level Cooccurrence Matrix25 0–1 Correlation) – 12.882318(Intensity Direct Local entropy Std) + 0.334371(Gradient Orient Histogram – 10 Percentile) + 0.341511(Gradient Orient Histogram – Mean Absolute Deviation) – 0.852883(Intensity Direct – Local Entropy Max) – 2.276332(Gray Level Cooccurrence Matrix25 – 45-7 Correlation) – 0.444632(Gradient Orient Histogram – 15 Percentile) – 0.682513(Gradient Orient Histogram – 0.025 Quantile) + 0.047303 (Intensity Direct 0.025 Quantile) – 21.459645 2 rPVP (Spleen) = 5.067 – 0.06022(Shape Volume) + 0.05643S (Shape Mass) + 0.1403(Shape Compactness1) – 1.601e-10(Intensity Direct – Energy) – 2.895(Intensity Direct – Local Entropy Std) – 0.002501(Intensity Direct – Local Range Max) –0.00494 (Intensity Direct – Local Std Max) + 9.818(Gray Level Cooccurence Matrix25 0–4 Information Measure Corr2) + 19.35(Shape Convex) + 50.021(Gray Level Cooccurence Matrix25 – 0–4Information MeasureCorr1 + 38.99(Gray Level Run Length Matrix25 – 0 Gy Level Nonuniformity) 3 rPVP (Liver and Spleen) = 19.33 – 0.1589(S-Shape Volume) + 0.1447(S-Shape Mass) + 0.1225(S-Shape Compactness1) + 0.009046(L-Shape Compactness2) + 2.326e-10(S-Intensity Direct – Energy) – 1.213(S-Intensity Direct – Local Entropy Std) – 0.0007155 (S-Intensity Direct – Local Range Max) – 0.008068 (S-Intensity Direct – Local Std Max) + 12.79 (S-Gray Level Cooccurence Matrix25 0–4 Information Measure Corr2) + 19.37 (S-Shape Convex) + 64.67 (S-Gray Level Coocurence Matrix25 – 0–4 Information Measure Corr1) – 8.826 (L-Gray Level Cooccurence Matrix25 – 0–1 Correlation) – 3.438(L-Intensity Direct – Local Entropy Std) – 1.059 (L-Intensity Direct – Local Entropy Max). 3.3. Validation cohort Unlike HVPG, there is currently a lack of recognized standard for direct PVP, despite its unwavering accuracy. In order to evaluate the utility of rPVP, a validation cohort from a prospectively maintained database was referenced. A total of 62 patients were included in the validation cohort, with 37 (59.7 %) male patients, 25 (40.3 %) female patients, with an average age of 57.45 ± 11.30 years old. Majority of the patients were Child Class A and B (59.7 % and 37.1 %, respectively), and the most common etiology was viral hepatitis (54.8 %). All patients received initial endoscopic therapy for the treatment of gastroesophageal varices from May 2013 to May 2015, in which 10 patients received endoscopic band ligation (EBL), 1 patient received cyanoacrylate injection, and 51 patients received a combination of EBL plus cyanoacrylate injection. All patients were followed closely at a designated outpatient service clinic and received follow-up endoscopic examination to determine whether further endoscopic intervention was necessary. The average time to follow-up endoscopy was 324.31 ± 221.20 days. Based on patient reported clinical symptoms
most common etiology and variceal hemorrhage (79.9 %) as the most common clinical presentation. The average intraoperative portal venous pressure (PVP) was 28.82 ± 5.91 mmHg, which decreased by 10.29 ± 4.82 mmHg after shunt placement. 3.2. Radiomics characteristics The liver and the spleen ROIs were drawn at the level of the porta hepatis and splenic hilum, respectively (Fig. 1). A total of 738 features were extracted for each organ, under 7 feature categories (Fig. 2). Intra6
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Fig. 6. Boxplot. Boxplot representing rPVP of the validation cohort predicted based on the proposed formula, including features extracted from the liver, spleen or a combination of both ROI.
delineation of ROIs. The rPVP was calculated according to previously proposed formula and the results were as follows: rPVP (Liver) 28.54 ± 3.36 mmHg, rPVP (Spleen) 28.64 ± 3.16 mmHg, and rPVP (Liver and Spleen) 28.69 ± 3.16 mmHg (Fig. 6). The utility of rPVP was tested for its predictive value of patient response to endoscopic treatment for gastroesophageal varices. All three predicted rPVP were significantly correlated with patient response to initial endoscopic treatment (p < 0.001). The predictive capabilities of the proposed models were further tested with a receiver operator’s curve (ROC) (Fig. 7). The corresponding results of rPVP Liver, rPVP Spleen and rPVP Liver Spleen were 0.808, 0.843 and 0.866, respectively. Among which, rPVP (Liver and Spleen) has the highest predictability (Fig. 8), with an optimal cut-off value of 29.102 mmHg and a corresponding sensitivity of 73.5 % and specificity of 92.9 % (Table 3). A Kaplan Meier curve further demonstrated the difference between variceal recurrence based on the optimal cut-off value with reference to time (Log Rank MantelCox p < 0.001) (Fig. 9). 4. Discussion Liver cirrhosis is the most common cause of portal hypertension, which remains a global health burden, especially in developing countries [25]. Etiologies include viral hepatitis, alcohol, autoimmune hepatitis, steatohepatitis, and drug-induced liver disease [26]. Apart from sinusoidal etiologies, portal hypertension can result from pre-hepatic or post-hepatic diseases, such as Budd-Chiari or idiopathic portal hypertension [27,28]. Although HVPG is currently recognized as the standard approach for assessing portal hypertension, its accuracy may vary with a difference in etiology and present as falsely normal in nonsinusoidal portal hypertension. Other factors such as concurrent HCC or PVT may also affect HVPG measurements, causing a localized obstruction or change in portal pressure [29]. Theoretically, the direct measurement of portal pressure provides the most accurate and reliable reflection of disease severity. However, its invasive nature and associated high complication rates limits its application in clinical practice. Recent studies have explored other minimally invasive approaches to directly measure portal pressure, such as EUS-guided portal vein catherization [9,29]. However, this method has only been tested on porcine models and its safety and application on human patients warrants further clinical trials. The search for a noninvasive alternative for assessing portal pressure is unrelenting. For instance, the use of spleen
Fig. 7. ROC curve in the validation cohort in three models plotted in one figure. (A) ROC curve of the model containing liver features. (B) ROC curve of the model containing spleen features. (C) ROC curve of the model containing both liver and spleen features.
and repeat endoscopic examination, each subject was classified into good responders or poor responders. A total of 7 responders experienced hematochezia or melena, in whom variceal recurrence was confirmed by a subsequent endoscopy. Twenty-six patients, although asymptomatic, were diagnosed as having recurrent gastroesophageal varices with a high-risk of hemorrhage, including presence of large varix with or without red wales sign, necessitating further endoscopic intervention. A total of 33 patients fulfilled the definition of poor responders, while 29 patients achieved complete variceal obliteration without any clinical symptoms. Comparison between patient characteristics were summarized in Table 2. The CT images of all patients in the validation cohort were retrieved for radiomics features extraction. Similar to the experiment cohort, the portal hepatis and splenic hilum level was referenced for the 7
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Fig. 8. Performance of radiomic prediction. The relative rPVP of each patient in the validation cohort in each model is plotted as a bar plot. Patient outcomes are also indicated with different colors. (A) Radiomic prediction performance of the model containing liver features. (B) Radiomic prediction performance of the model containing spleen features. (C) Radiomic prediction performance of the model containing both liver and spleen features. Table 3 Model Accuracy based of Receiver Operating Characteristic (ROC) Curve. Radiomic Features
AUROC (95 % CI)
Threshold
Specificity
Sensitivity
Accuracy
NPV
PPV
Liver Spleen Liver Spleen
0.789 (0.676–0.90) 0.832 (0.723–0.941) 0.855 (0.760–0.951)
28.274 28.147 29.102
0.750 0.786 0.929
0.765 0.794 0.735
0.758 0.790 0.823
0.724 0.759 0.743
0.788 0.818 0.926
combination of both organs. Due to the lack of widespread practice for direct PVP measurement, there is currently no established diagnostic “gold” standard for PVP. Unlike HVPG, CSPH cannot be defined based on PVP values. Therefore, the clinical significance of PVP was further tested on a validation cohort which comprised of patients receiving endoscopic therapy for the treatment of gastroesophageal varices. All three predictive models (rPVP Liver, rPVP Spleen, rPVP Liver Spleen) exhibited satisfying precision. In comparison, rPVP Liver Spleen yielded the highest AUROC of 0.866 and optimal cut-off value of 29.102 mmHg, resulting in a more stringent model, hence the higher specificity and lower sensitivity (Table 3). Patients with high predicted rPVP may not benefit from the traditional endoscopic treatment for gastroesophageal varices. Instead, other treatment options, such as TIPS or surgery, should be sought. rPVP can serve as a useful reference for physicians in recommending an appropriate personalized therapy for the treatment of gastroesophageal varices. Based on the preliminary results, we incorporated radiomics in the diagnosis, outcome prediction and treatment selection for patients with portal hypertension. To the best of our knowledge, this study is the first to report the use of radiomics for the assessment of portal hypertension. However, there are several limitations that needs to be addressed. The lack of direct PVP measurement in the validation cohort crippled the evidential strength of the proposed model. The small sample size of both the experimental and validation cohort may limit the interpretation of results. The validation cohort only comprised of patients with gastroesophageal varices and predicted treatment response to endoscopic treatment. Therefore, application of the results in other symptoms of decompensation such as refractory ascites or hepatic encephalopathy, should be carried out with caution. The use of clinical imaging has been rapidly evolving of the past decade. Radiomics employs available tomographic studies and extracts innumerable quantitative features into mineable high-dimensional data. Apart from providing visual diagnostic information, tomographic images have characteristics that are often neglected by the naked eye. With the rise of machine learning and artificial intelligence in the
Fig. 9. Kaplan-Meier Curve. Based on the optimal cut-off value of 29.102 mmHg, a KM curve demonstrated the difference between variceal recurrence and nonrecurrence after initial endoscopic therapy for the treatment of gastroesophageal varices. Log Rank Mantel-Cox (p < 0.001).
and liver stiffness in predicting HVPG showed promising accuracy [21]. Other studies have also demonstrated the correlation between CT perfusion and HVPG [30]. However, its utility in reflecting actual PVP and disease severity remains precarious, especially in non-sinusoidal etiologies of portal hypertension. The present study proposed a non-invasive model in predicting portal venous pressure through an image-derived algorithm. Radiomic features were extracted from both liver and spleen and analyzed on an open infrastructure software platform: IBEX [18,19]. Features with the highest predictability were filtered from over hundreds of features via the LASSO method to construct a model for radiomics-derived portal venous pressure (rPVP). Three rPVP predictive models were constructed based on features derived from the liver, spleen and a 8
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medical industry, disease diagnosis and management can be revolutionized [13,16]. The general idea of machine learning lies within the ability to predict outcomes from measurable predictors. In this study, we were able to predict PVP values via class prediction. Direct PVP measurement is clinically inconvenient to obtain, but can be derived from the easily accessible CT studies with promising accuracy. Due to the randomness included in the statistical process, such as crossvalidation, a slight difference in prediction might occur given the same set of features in class prediction. This study provides insight to the many possible applications of machine learning, which is a process of continuous optimization that can potentially revolutionize the medical industry.
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CRediT authorship contribution statement Yujen Tseng: Data curation, Writing - original draft. Lili Ma: Formal analysis, Writing - original draft. Shaobo Li: Formal analysis, Methodology. Tiancheng Luo: Formal analysis, Project administration. Jianjun Luo: Investigation, Resources. Wen Zhang: Investigation, Resources. Jian Wang: Conceptualization, Funding acquisition. Shiyao Chen: Supervision. Declaration of Competing Interest All authors declare no conflict of interest. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study. Acknowledgement This study was supported by the Innovation Fund of Shanghai Scientific Committee (No. 15411950501). Appendix A. Supplementary data Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.ejrad.2020.108927. References [1] G. Garcia-Tsao, J.K. Lim, Management and treatment of patients with cirrhosis and portal hypertension: recommendations from the Department of Veterans Affairs Hepatitis C Resource Center Program and the National Hepatitis C Program, Am. J. Gastroenterol. 104 (2009) 1802–1829, https://doi.org/10.1038/ajg.2009.191. [2] R. de Franchis, Expanding consensus in portal hypertension: report of the Baveno VI Consensus Workshop: stratifying risk and individualizing care for portal hypertension, J. Hepatol. 63 (2015) 743–752, https://doi.org/10.1016/j.jhep.2015. 05.022. [3] G. Garcia-Tsao, A.J. Sanyal, N.D. Grace, W. Carey, Prevention and management of gastroesophageal varices and variceal hemorrhage in cirrhosis, Hepatology 46 (2007) 922–938, https://doi.org/10.1002/hep.21907. [6] J. Bosch, J.G. Abraldes, A. Berzigotti, J.C. Garcia-Pagan, The clinical use of HVPG measurements in chronic liver disease, Nat. Rev. Gastroenterol. Hepatol. 6 (2009) 573–582, https://doi.org/10.1038/nrgastro.2009.149. [7] S. Keiding, J. Solvig, H. Gronbaek, H. Vilstrup, Combined liver vein and spleen pulp pressure measurements in patients with portal or splenic vein thrombosis, Scand. J. Gastroenterol. 39 (2004) 594–599, https://doi.org/10.1080/00365520410005171. [8] A. Perello, A. Escorsell, C. Bru, R. Gilabert, E. Moitinho, J.C. Garcia-Pagan, et al., Wedged hepatic venous pressure adequately reflects portal pressure in hepatitis C virus-related cirrhosis, Hepatology 30 (1999) 1393–1397, https://doi.org/10.
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