Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: A preliminary study

Journal Pre-proof Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: a preliminary study Yan Tan, Wei Mu, Xiao-chun Wang, Guo-qiang Yang, Robert James Gillies, Hui Zhang

PII:

S0720-048X(19)30435-8

DOI:

https://doi.org/10.1016/j.ejrad.2019.108785

Reference:

EURR 108785

To appear in:

European Journal of Radiology

Received Date:

19 September 2019

Revised Date:

20 November 2019

Accepted Date:

28 November 2019

Please cite this article as: Tan Y, Mu W, Wang X-chun, Yang G-qiang, Gillies RJ, Zhang H, Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: a preliminary study, European Journal of Radiology (2019), doi: https://doi.org/10.1016/j.ejrad.2019.108785

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Whole-tumor radiomics analysis of DKI and DTI may improve the prediction of genotypes for astrocytomas: a preliminary study

Authors:

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Yan Tan1, 2# (MD), Wei Mu3# (PhD), Xiao-chun Wang1, 2 (MD), Guo-qiang Yang1, 2(PhD), Robert James Gillies3*(PhD) , Hui Zhang1, 2* (MD)

Department of Radiology, First Clinical Medical College, Shanxi Medical University, Taiyuan,

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Affiliations:

030001, Shanxi Province, China

College of Medical Imaging, Shanxi Medical University, Taiyuan 030001, Shanxi Province, China

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Departments of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa,

Florida, U.S.A.

Yan Tan, Wei Mu contributed equally to this work.

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#

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* Corresponding author:

Robert James Gillies, Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, 12902 Magnolia Drive Tampa, FL, USA, 33612. E-mail: [email protected], Tel:

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8137458355; Fax: 8137458375.

Hui Zhang, Department of Radiology, First Clinical Medical College of Shanxi Medical University, Taiyuan 030001, Shanxi Province, China. E-mail: [email protected], Tel: +86-18635580000. Fax: +86 351 2024239.

Highlights 1

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Radiomics models of MK and MD were imaging biomarkers for predicting IDH and MGMTmet genotypes. Combined model improved to predict IDH, which showed the incremental value of radiomics features.

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Combined model did not improve predictive performance of MGMTmet.

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Abstract

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predict IDH and MGMTmet genotypes of astrocytomas.

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Purpose: To test whether the whole-tumor radiomics analysis of DKI and DTI images could

Method: Sixty-two astrocytomas were enrolled. 364 radiomics features of whole tumor were

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extracted from mean-kurtosis (MK), and mean-diffusivity (MD) images, respectively. The

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multivariable logistic regression was used to select the most meaningful radiomics features for predicting IDH and MGMTmet genotypes. A radiomics model was built by logistic linear

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regression. A combined model was established based on selected radiomic, radiological and clinical features. To assess the difference between the models, the Z-test was performed. Results: The radiomics model built using the three most informative radiomics features for each

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genotype yielded an AUC of 0.831 ((95% confidence interval [CI]: 0.721-0.918) for predicting IDH genotype, and 0.835 (95%CI: 0.686-0.951) for MGMTmet genotype. A combined model for predicting IDH based on the radiomics score, age, and degree of edema reached an AUC of 0.885 (95%CI: 0.802-0.955) and a combined model for predicting MGMTmet based on radiomics score and edema degree reached an AUC of 0.859 (95%CI: 0.751-0.945) which was not significantly

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higher than the radiomics only model (P = 0.081). Conclusions: The radiomics models via an objective whole-tumor analysis of MK and MD maps were independent imaging biomarkers for predicting IDH and MGMTmet genotypes, and the combined model further improved the performance for IDH, but not for MGMTmet.

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Keywords: Astrocytoma; Radiomics; Diffusion tensor imaging; Genotype

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Introduction

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Astrocytomas are the most common type of diffuse glioma with poor prognosis [1]. The 2016 new classification of CNS cancers recognized several new entities of diffuse gliomas based

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on genotypes, in addition to the histological phenotypes of tumors. Isocitrate dehydrogenase (IDH)

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and oxygen 6-methylguanine-DNA methyltransferase (MGMT) are selected as important genetic hallmarks for astrocytomas [2-3], given that patients with IDH mutant (IDHMUT) survive longer than those with IDH wild-type (IDHWT), and MGMT promoter methylation (MGMTmet) are more sensitive to chemo-radiotherapy and have longer than the unmethylated MGMT promotor.

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Preoperative determination of IDH and MGMTmet genotypes of astrocytomas may aid in planning treatment strategies and predicting prognosis [3-5]. The current gold standard for genotyping gliomas is mainly identified by sequencing or immunohistochemistry with tumour samples, which is an invasive and expensive procedure, and susceptible to sampling bias [2], whereas imaging captures the entire brain. Currently, the potential to increase clinical utility of MRI imaging as a 3

non-invasive technique to accurately ascertain gliomas genotype is gaining a lot of attention, with the expected use case of comparing concordance or discordance with the biopsy results for treatment decision support. Advanced MRI techniques such as diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) can probe the pathological changes in gliomas, providing abundant important

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information which is not apparent on conventional MRI imaging [6]. Most radiologists estimate

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glioma via simple visual inspection of DKI and DTI parameter maps on the “hot-spot” area (the most obvious diffusion-restricted region, avoiding visible necrosis, cystic degeneration, or

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hemorrhage) of tumour [7-8]. However, this inevitably has significant subjective influence, and

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inter-reader variability. Further, the DKI/DTI parameter values reflect regional information of tumor area, rather than the whole-tumor heterogeneity. This also may be one reason of inconsistent

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results of current DTI and DKI studies. Based on the fact that obvious differences exist in

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microvascular proliferation, cellular density, and local microenvironment, intra- and inter-tumor heterogeneity may result in imaging heterogeneity, which is related to the tumour genotypes [9]. Thus, assessment of glioma genotypes by quantifying MR diffusion imaging heterogeneity of whole tumour may serve as a powerful tool to instruct personalized and precise therapeutic

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decision-making.

Radiomics is the process of converting medical images into quantitative, objective, and

mineable imaging features from the entire tumor and its surroundings using machine learning algorithms [10-11]. Radiomics allow for more precise diagnosis, prediction of survival, and assessment of therapeutic response in glioma than traditional imaging biomarkers [12-14], which 4

can thus offer a complementary tool to existing radiological practice. These are no studies interrogate with radiomics based on DKI and DTI to predict the clinically actionable IDH and MGMTmet genotypes of glioma. Tumors are heterogeneous both genetically and histopathologically, with intratumoural spatial variation in the cellularity, angiogenesis, areas of necrosis and extravascular extracellular matrix, which might result in the diffusion heterogeneity

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of whole tumour [15]. Thus, we postulated that the heterogeneity of DKI and DTI parameter values

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within whole gliomas could be useful for predicting IDH and MGMTmet genotypes. Therefore, this study evaluated the role and incremental value of whole-tumor radiomics analysis based on

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DKI and DTI images in determining the IDH and MGMTmet genotypes of astrocytomas.

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Materials and Methods Patient population

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The institutional review board of our institution approved this study protocol. Given the

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retrospective study and anonymous patient data, informed consents were waived. According to the inclusion and exclusion criteria (Supplementary S1), 62 patients were obtained between January 2014 and July 2017. Tumors were graded using the World Health Organization Classification of Tumors of the Nervous System (2016) criteria. IDH status of the patients was determined using

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Sanger sequencing, the MGMTmet was determined by pyrosequencing analysis (Supplementary S2). The clinical characteristics of gender, age and grade were also studied. Univariate and multivariate analysis were conducted to determine potential clinical and radiological characteristics which were significantly related to IDH and MGMTmet genotypes, respectively. Preoperative MRI data acquisition 5

Preoperative MRI was performed with a 3.0-T scanner (GE Signa HDxt) using an 8-channel array coil. The scanning sequences included conventional MRI sequences, DKI and DTI sequences. Echo planar imaging (EPI) sequence was used to perform DTI and DKI. Implemented b values were 0, 1000, and 2000 mm2/s for DKI (including DTI). These were applied in 30 uniformly distributed directions. Parameters for DKI data: TR/TE: 6500/84.6 ms; FOV, 240 mm × 240 mm;

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matrix, 96 × 96. Number of excitations: one. Slice thickness, 6 mm; slice interval, 1 mm.

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Imaging process and segmentation

Quantitative semantic radiological characteristics were assessed by the neuroradiologist

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with 15 (XXX) years’ experience, which included tumour size, border, hemorrhage, cystic and

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necrosis, edema degree, enhancement style and degree, signal characteristics, tumour location, cross midline growth, involving deep white matter, involving pia mater, involving ependymal

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membrane.

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The DKI and DTI data were analyzed by the DKI software in GE Functool 9.4.05a on a GE Advanced Workstation 4.4, and mean kurtosis (MK) and mean diffusivity (MD) images were obtained. One radiologist manually measured MK and MD parameter values on the solid parts of astrocytoma tumors (on the most obvious diffusion-restricted region, avoiding visible necrosis,

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cystic degeneration, or hemorrhage in the lesions). (Supplementary S3) MK and MD images were used for the radiomics analysis (Figure 1). Rigid registration of

T2 fluid-attenuated inversion recovery (T2FLAIR), MK, and MD images was obtained using contrast-enhanced T1-weighted images (CE-T1WI) as a template. Segmentation was performed slice by slice to obtain volumetric datasets using ITK-SNAP (http://www.itksnap.org). CE-T1WI 6

and T2FLAIR images were used to visualize tumor and edema boundaries, which were used to determine the ROI of whole tumor area. ROIs of enhanced tumours, the enhanced rim was the border of tumour area. ROIs of unenhanced tumours, the intensity of the tumour was lower than peritumoural edema on CE-T1WI image, and higher than peritumoural edema on MK images. The ROIs of tumour area were delineated on CE-T1WI images by a neuroradiologist (XXX) with

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15 years of experience, and confirmed by the another neuroradiologist with 30 years’ experience

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(XX), then transferred to corresponding MK and MD images. The ROIs segmentation of the MRI images is showed in Figure 2.

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Radiomics feature extraction

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The feature pool included 364 radiomics features from the image biomarker standardisation initiative (IBSI) [16], which consisted of intensity, morphological, textural, laws

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and wavelet features. We extracted these 3D radiomics features from the segmentations of whole

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tumor area on MK and MD images, respectively. Thus, a total of 728 features were obtained. Radiomics feature selection and radiomics model construction All the radiomics features were processed as follows: 1）ROC analysis was performed 100 times with Bootstrap resampling; 2) Features with more than 95% of their AUC values on one side

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or the other of 0.5 were selected. From these, the mean AUC was calculated to measure the classification ability of each feature; 3) These were then clustered into similarity groups, if the Pearson correlation coefficients were larger than 0.8, and the Pearson coefficients between features in different groups was smaller than 0.8. For each group, only the feature with the largest mean AUC was selected as an avatar for that group; 4) Then the final features and the corresponding 7

coefficients were selected and determined by multivariable stepwise logistic regression. In each step, the criterion to add or remove terms is P-value for chi-squared test of the change in the deviance by adding or removing the variable, where the P-value is 0.05 for adding a variable, and 0.1 for removing a variable. Due to the imbalance of MGMTmet and no MGMTmet, the patients were oversampled to keep the balance of the two genotypes for the MGMT radiomics model

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construction.

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A bootstrap multivariable logistic regression was used for internal validation, and the performance of the radiomics model was assessed using receiver operating characteristic curve

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(ROC).

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Development of combined model

Incorporating the radiomics score, selected clinical and radiological characteristics, the

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combined model was built using the logistic regression method with forward stepwise selection.

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To assess the difference of performance among the combined models, radiomics model, clinical model, radiological model, the Z-test was performed. Statistical analysis

The statistical analysis was performed with R software, version 3.0.1 (http://www.R-

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project.org). The packages in R used in this study are described as follows: Chi-Squared Test, χ2 test were done using the “stats” package. Levene's test was done using the “car” package. C-index calculation was performed the “Hmisc” package. ROC curves plot was done using “pROC” package. Logistic regression was used to generate odds ratios (ORs) and 95% confidence intervals (CIs). Bootstrap resampling of the models was performed at 100 for internal validation [17-18]. 8

Inter-group differences between IDHMUT and IDHWT, MGMTmet and MGMTno-met groups with respect to MK and MD values in solid parts of the tumors, clinical and radiological characteristics were compared using the t-test or χ2 test. P < 0.05 was statistically significant. The reported statistical significance levels were all two sided, with the statistical significance level set at 0.05. Results

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Patient clinical and radiological characteristics and models

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The clinical and radiological characteristics of astrocytomas are shown in Table 1.

For IDH prediction, the univariate analysis revealed that significant differences existed in

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age (AUC=0.778, P = 0.001), grade (AUC = 0.706, P = 0.004), edema degree (AUC=0.776, P <

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0.001), and enhancement degree (AUC=0.659, P = 0.002) between IDHMUT and IDHWT groups. By further multivariable logistic regression analysis, the age and grade were selected to develop

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clinical model with AUC = 0.775, the MK value and edema degree were selected to develop

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radiological model with AUC = 0.810 (Figure 3).

For MGMTmet prediction, the univariate analysis showed that significant differences existed in grade (AUC=0.671, P = 0.026), edema degree (AUC=0.768, P < 0.001), border (AUC=0.651, P = 0.047), and enhancement degree (AUC = 0.650, P = 0.035) between the

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MGMTmet and no MGMTmet groups. The clinical model was built by grade (AUC = 0.671), the radiological model was built by edema degree (AUC = 0.768) by further multivariate analysis (Figure 3). The accuracy, sensitivity and specificity of these models are shown in Table 2 and Table 3. Radiomics feature selection and radiomics model construction 9

Due to the best performance (AUC = 0.831 for IDH, 0.835 for MGMTmet), radiomics models were constructed from three radiomics features from MK and MD images (Figure 3). The accuracy, sensitivity and specificity of radiomics models are shown in Table 2 and Table 3. The radiomics models for IDH and MGMTmet prediction were as follows： Radiomics-score for IDH = MD_Elongation × (-4.194)

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+ MK_90th percentile × (-7.473)

+ 3.768

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Radiomics-score for MGMT = MD_Entropy × (-4.517)

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+ MK_E5R5L5 × (78.625)

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+ MK_Maximum histogram gradient × (-10.244) + MK_Small zone high grey level emphasis × (-11.696)

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+ 11.596

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Performance of combined model

For IDH prediction, the radiomics score (odds ratio [OR] = 2.34; 95% confidence interval [CI]: 1.42-6.01), edema (OR = 0.60; 95% CI: 0.31-0.99) and age (OR = 0.92; 95% CI: 0.86-0.98) were selected to develop a combined model, which performed significantly better (AUC = 0.885)

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than the radiomics model (AUC = 0.831), the clinical model (AUC = 0.775), or the radiological model (AUC = 0.810), with P-values = 0.025, 0.016, 0.023, respectively (Figure 3 and Figure 4). For MGMTmet prediction, radiomics score (OR = 2.68; 95% CI: 1.54-7.75) and edema (OR = 0.48; 95% CI: 0.18-0.87) were selected to develop combined model. This combined model reached the higher AUC (AUC = 0.859) compared to the clinical model (AUC = 0.671), or the 10

radiological model (AUC = 0.768), with P-values < 0.001 and = 0.024, respectively. However, there was no significant difference between radiomics model (AUC = 0.836) and combined model (P = 0.081) (Figure 3 and Figure 4). The accuracy, sensitivity and specificity of combined models are shown in Table 2 and Table 3.

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Discussion

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In our study, a radiomics models based on whole-tumor MK and MD maps showed good diagnostic efficiency in predicting IDH and MGMTmet genotypes of astrocytomas. Furthermore,

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the combined model constructed by radiomics score, edema degree and age further improved the

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performance of predicting IDH, while the combined model constructed by radiomics score and edema degree did not benefit the predictive performance of MGMTmet.

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The combined three radiomics features based on MK and MD images could accurately

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predict the IDH genotype, which was consistent with results of Bisdas et al. that the performance of DKI was enhanced by texture analysis for prediction of IDH [19]. MK reflects the complexity of the tissue structure [20]. MD contains the information of overall level of water molecule diffusion and the diffusion restriction [21]. Previous DKI and DTI studies mainly focused on the

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“hot-spot” method, which selected on the most obvious diffusion-restricted region and adopted an average value [22-23]. However, “hot-spot” values usually represent the upper part of the whole value of the ROI. In our study, both 90th percentile values (radiomics feature) and “hot-spot” values (radiological feature) of MK significantly correlated with IDH genotype. IDHWT astrocytomas have more aggressive behavior than that of IDHMUT tumours, because of more tumor 11

angiogenesis, greater nuclear atypia and increased cell density in the tumoural solid part [9]. Both the radiomics feature and “hot-spot” values could reflect the different complexity between IDHWT and IDHMUT astrocytomas, but the “hot-spot” values were excluded by the further multivariate analysis in combined model, because of its high correlation to the 90th percentile values, which were objectively obtained. Furthermore, IDHMUT astrocytomas show far less necrosis than their

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wild-type counterparts due to antithrombotic effects of the oncometabolites [24]. The radiomics

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features of Elongation and E5R5L5 could provide additional information of the complexity and heterogeneity of diffusion in the entire tumors, while “hot-spot” values could not. Thus, radiomics

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analyses that can measure the heterogeneity of diffusion parameter values within whole tumours

analysis of diffusion MR images.

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is of high priority, considering the conventional method cannot meet the need in the present

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Our results also showed that the radiomics model contained other two texture features and

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one first-order feature that could accurately predict MGMTmet genotype, while the manually measured MK and MD parameter values in the tumoral solid area are not as helpful. Studies have found that the cystic degeneration and confluent necrosis were the significant features and were independent predictors for non MGMT-methylated astrocytomas [25-27]. The manually measured

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parameter values of MK and MD in the tumoral local solid area could not reflect the whole-tumor heterogeneity or arrangement including the cystic degeneration, necrosis, and hemorrhage. This may be one reason why the manually measured parameter values of MK or MD could not reflect the MGMTmet genotype. Furthermore, in the process of parameter selection and extraction, the average MK and MD values of whole tumours which generated automatically by radiomics 12

analysis were eliminated due to the lack of significant influence, and the radiological qualitative feature of cystic or necrosis judged by the radiologist also lacked significant difference. The notion that radiomics analysis can reveal visually imperceptible tumor information extends beyond radiology to histopathology [27], the whole-tumor radiomic features of MK and MD maps including two texture features and one first-order feature which reflect the diffusion heterogeneity

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of the entire tumors could predict the MGMTmet genotype, and obviously performed better than

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clinical and radiological models. Thus, the radiomics model is a potential new effective imaging biomarker for predicting MGMTmet genotypes.

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By the multivariate analysis, the combined model of IDH genotype was constructed by

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radiomics score, edema degree and age, and performed better than radiomics, clinical and radiological models respectively, which demonstrated the incremental value of the radiomics

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features based on MK and MD images for predicting IDH genotype of astrocytomas. It was

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important to note that the age and edema degree demonstrated sufficient predictive strength and could be easily obtained preoperatively [28-29], which made the inclusion of these variables a common strategy for combined model development of IDH. For MGMTmet genotype, the radiomics score and edema degree were selected, although the combined model performed better

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than clinical model and radiological model, there was no significant difference between the radiomics model and combined model (P = 0.081). Li et al. also observed that combing clinical factors with radiomics features based on conventional MRI images did not benefit the prediction performance of MGMTmet [30]. This indicated that the performance of clinical and radiological features for MGMTmet was poor, adding these features to combined model just increased the 13

complexity but no accuracy. So, the combined model further improved the performance for IDH prediction, but did not benefit the predictive performance for MGMTmet. Our study still had several limitations. First, this is a retrospective study with relatively small sample size, which was based on a single-center. Although we did not have validation cohort, we used a Bootstrap method for internal validation, and found the prediction models were stable.

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Second, the prerequisite work in our study was the segmentation of the ROIs, which can introduce

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variability and influence results. Deep Learned automated brain cancer segmentation algorithms are now available and could be employed for such studies in the future.

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Conclusion

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The radiomics model via an objective whole-tumor analysis of MK and MD maps were independent imaging biomarkers for predicting IDH and MGMTmet genotypes, and the combined

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model further improved the performance for IDH, but not for MGMTmet.

Conflict of Interest

The authors of this manuscript declare no relationships with any companies, whose

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products or services may be related to the subject matter of the article.

Acknowledgements The authors declare no potential conflicts of interest. Funding: This study was supported by the National Natural Science Foundation (81471652, 81771824 and 81971593 to Hui Zhang; 81701681 to Yan Tan; 81971592 to Xiao-chun Wang; 11705112 to Guo-qiang Yang); the Precision 14

Medicine Key Innovation Team Project (YT1601 to Hui Zhang); the Social Development Projects of Key R&D Program in Shanxi Province (201703D321016 to Hui Zhang); the Youth Innovation Fund (YC1426 to Yan Tan); and the US National Cancer Institute, U01 CA143062 and

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Figures Legends Figure 1. A flowchart describing the radiomics method for IDH and MGMTmet genotypes prediction. 1) Regions of interests (ROI) of the tumour area were delineated, on 2-dimensional

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MK and MD maps layer by layer. 2) Radiomics features were extracted including non-textural,

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textural and wavelet features after local standard deviation filter. 3) The multivariable logistic regression was used to select meaningful radiomics features for predicting IDH and MGMTmet

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genotypes. 4) Prediction model was constructed incorporating the radiomics score, clinical

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features and radiological features.

Figure 2. The ROI segmentations of tumour area for astrocytomas. The red curve and arrow represent ROI of the tumour, the blue arrow represent the edema. Edema degree was more obvious in the IDHWT and no MGMTmet groups compared to the IDHMUT and MGMTmet groups.

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Figure 3. The ROC curves of clinical, radiological, radiomics and combined models for IDH

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prediction (A), and for MGMTmet prediction (B).

Figure 4. Decision curve analysis for each models. The combined model had the highest net

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benefit compared with the other models for IDH (A), and the radiomics model and combined

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of

model had the higher net benefit compared with the other model for MGMTmet (B).

21

Table 1. Patients’ clinical and radiological characteristics by univariate analyses Table 2. The performance of the clinical, radiological, radiomics, combined models for IDH prediction Table 3. The performance of the clinical, radiological, radiomics, combined models for

-p

Tables

ro

of

MGMTmet prediction

IDH Mutant (N=30)

Gender Male Female Age (Years)

Jo

Grade

(N=32)

MGMT

Met

No-Met

(N=46)

(N=16)

0.622

ur na

Clinical

Wide

P-value

lP

Characteristics

re

Table 1. Patients’ clinical and radiological characteristics by univariate analyses

0.050

15 (50.0%)

18 (56.25%)

21 (45.7%)

12 (75.0%)

15 (50.0%)

14 (43.75%)

25 (54.3%)

4 (25.0%)

43.23±11.87

55.97±11.74

48.83±13.70

52.63±12.25

0.001*

P-value

0.004*

0.326 0.026*

High grade

12 (40.0%)

24 (75.0%)

24 (52.2%)

12 (75.0%)

Low grade

18 (60.0%)

8 (25.0%)

22 (47.8%)

4 (25.0%)

MD value

0.90±0.23

0.76±0.24

0.029*

0.85±0.26

0.78±0.17

0.300

MK value

0.48±0.15

0.66±0.14

<.001*

0.56±0.18

0.62±0.14

0.350

Radiological

22

Tumor size (cm)

5.71±1.74

6.50±1.99

0.990

6.23±1.20

5.68±1.52

0.251

Border

0.047

Well-defined

8 (26.7%)

13 (40.6%)

12 (26.1%)

9 (56.3%)

Ill-defined

22 (73.7%)

19 (59.4%)

34 (74.9%)

7 (43.7%)

0.249

Hemorrhage

0.387

Yes

4 (13.3%)

8 (25.0%)

10 (21.7%)

2 (12.5%)

No

26 (86.7%)

24 (75.0%)

36 (78.3%)

14 (87.5%) 0.155

21 (70.0%)

24 (75.0%)

33 (71.7%)

No

9 (30.0%)

8 (25.0%)

13 (28.3%)

Edema

1.43±0.92

2.38±1.35

Yes

11 (36.7%)

28 (87.5%)

No

19 (63.3%)

4 (12.5%)

4 (25.0%)

2.73±1.29

25(54.3%)

14 (87.5%)

21(45.7%)

2 (12.5%)

re

-p

1.31±1.10

0.002*

Enhancement

Slight

4 (13.3%)

Obvious

17 (56.7%)

1 (3.1%)

0 (0.0%)

2 (6.3%)

5 (10.9%)

1 (6.3%)

29 (90.6%)

31 (67.4%)

15 (93.7%)

0.948

0.229

Homogeneous

2 (7.1%)

2 (6.3%)

4 (8.7%)

0 (0.0%)

Heterogeneous

28 (92.9%)

30 (93.7%)

42 (91.3%)

16 (100.0%)

0.784

Tumor location

Jo

Left hemisphere

Right hemisphere

0.665

13 (43.3%)

15 (46.9%)

20 (43.5%)

8 (50.0%)

17 (56.7%)

17 (53.1%)

26 (56.5%)

8 (50.0%)

0.649

CMG

0.584

Yes

4 (13.3%)

6 (18.8%)

7 (15.2%)

3 (18.8%)

No

26 (86.7%)

26 (81.2%)

39 (84.8%)

13 (81.2%)

0.145

IDW Yes

23 (76.7%)

29 (90.6%)

0.636 38 (82.6%)

23

0.001*

0.035*

10 (21.7%)

lP

9 (30.0%)

ur na

No

Intensity

0.000*

12 (75.0%)

ro

Yes

of

0.697

Cystic or necrosis

0.311

14 (87.5%)

No

7 (23.3%)

3 (9.4%)

8 (17.4%)

2 (12.5%)

0.466

IPM1

0.157

Yes

22 (73.3%)

26 (81.2%)

38 (82.6%)

10 (62.5%)

No

8 (26.7%)

6 (18.8%)

8 (17.4%)

6 (37.5%)

0.079

IPM2

0.521

Yes

6 (20.0%)

13 (40.6%)

13 (28.3%)

6 (37.5%)

No

24 (80.0%)

19 (59.4%)

33 (71.7%)

10 (62.5%)

Jo

ur na

lP

re

-p

ro

of

Note: P-value < 0.05 was considered as a significant difference. CMG: Cross midline growth. IDW: Involving deep white matter. IPM1: Involving pia mater. IPM2: Involving ependymal membrane. “*” was considered as a significant difference.

24

Table 2. The performance of the clinical, radiological, radiomics, combined models for IDH prediction

Model

Bootstrap (2000 times)

Radiomics model

0.776

75.8

0.633

0.875

(0.652-0.885)

(45.1-84.6)

(0.612-0.838)

(0.574-1.000)

0.810

74.2

0.833

0.656

(0.689-0.919)

(66.1-85.5)

(0.452-0.922)

0.831

79.0

ro

of

Specificity

(0.554-0.973)

0.867

0.719

(0.721-0.918)

(67.7-88.7)

(0.625-0.939)

(0.594-0.926)

0.885

80.6

0.933

0.687

(0.667-0.971)

(0.559-0.944)

lP

Combined model

Sensitivity

-p

Radiological model

Accuracy

re

Clinical model

AUC

(71.0-90.3)

Jo

ur na

(0.802-0.955)

25

Table 3. The performance of the clinical, radiological, radiomics, combined models for MGMTmet prediction

Bootstrap (2000 times)

Radiomics model

54.8

(0.515-0.818)

(17.7-62.9)

0.768

67.7

(0.624-0.895)

(56.5-83.9)

0.835

0.750

(0.000-0.586)

(0.500-1.000)

0.630

0.812

(0.457-0.911)

(0.474-1.000)

87.1

0.913

0.750

(75.8-93.5)

0.795-0.976)

(0.500-1.000)

0.859

74.2

0.696

0.875

(0.751-0.945)

(59.7-88.7)

(0.480-0.911)

(0.643-1.000)

Jo

ur na

lP

(0.686-0.951) Combined model

0.478

Specificity

of

0.671

Sensitivity

-p

Radiological model

Accuracy

re

Clinical model

AUC

ro

Model

26