Efficacy of an Artificial Neural Network–Based Approach to Endoscopic Ultrasound Elastography in Diagnosis of Focal Pancreatic Masses

CLINICAL GASTROENTEROLOGY AND HEPATOLOGY 2012;10:84 –90

Efficacy of an Artificial Neural Network–Based Approach to Endoscopic Ultrasound Elastography in Diagnosis of Focal Pancreatic Masses ADRIAN SA˘FTOIU,*,‡ PETER VILMANN,‡ FLORIN GORUNESCU,§ JAN JANSSEN,储 MICHAEL HOCKE,¶ MICHAEL LARSEN,# JULIO IGLESIAS–GARCIA,** PAOLO ARCIDIACONO,‡‡ UWE WILL,§§ MARC GIOVANNINI,储 储 CRISTOPH F. DIETRICH,¶¶ ROALD HAVRE,## CRISTIAN GHEORGHE,*** COLIN MCKAY,‡‡‡ DAN IONUT᝺ GHEONEA,* and TUDOREL CIUREA,* on behalf of the European EUS Elastography Multicentric Study Group *Gastroenterology Department, and §Department of Biostatistics and Computer Science, University of Medicine and Pharmacy, Craiova, Romania; ‡Department of Surgical Gastroenterology, Gentofte & Herlev Hospital, University of Copenhagen, Denmark; 储Helios Klinikum, University of Witten/Herdecke, Wuppertal, Germany; ¶ Department of Internal Medicine II, Hospital Meiningen, Meiningen, Germany; #Center for Surgical Ultrasound, Department of Surgery, Odense University Hospital, Odense, Denmark; **Gastroenterology, University Hospital, Santiago de Compostela, Spain; ‡‡Gastroenterology and Gastrointestinal Endoscopy Unit, Vita Salute San Raffaele University, Milan, Italy; §§Gastroenterology, SRH, Wald-Klinikum, Gera, Germany; 储 储Endoscopic Unit, Paoli-Calmettes Institut, Marseilles, France; ¶¶Med Klinik 2, Caritas-Krankenhaus Bad Mergentheim, Bad Mergentheim, Germany; ##Institute of Medicine, University of Bergen and National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway; ***Fundeni Clinical Institute of Digestive Diseases and Liver Transplantation, Bucharest, Romania; ‡‡‡ Hepatobiliary Surgery, Glasgow Royal Infirmary, Glasgow, United Kingdom

BACKGROUND & AIMS: By using strain assessment, realtime endoscopic ultrasound (EUS) elastography provides additional information about a lesion’s characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis. METHODS: We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n ⫽ 47) or pancreatic adenocarcinoma (n ⫽ 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns. RESULTS: The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%–92.42%) and 84.27% testing accuracy (95% CI, 83.09%– 85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%–0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85. CONCLUSIONS: Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses. Keywords: Tissue Elasticity; Cancer; Pancreas; Tumor; Mass.

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he assessment of tissue elasticity achieved significant interest in gastroenterology owing to the increased availability of this technology in the clinical decision-making algorithms. Elastography information consequently is displayed in a transparent color overlay based on a defined region of interest, similar to color Doppler examinations.1 By using strain assessment, real-time elastography provides additional information about a lesion’s characteristics. Real-time elastography has been reported to be useful for the diagnosis and differentiation of many tumors, which usually are harder than normal surrounding tissues, for example, in assessing breast2 and thyroid cancer3 diagnosis, or in guiding minimally invasive treatment of prostate cancer.4,5 Recently, transabdominal real-time elastography was proposed as a new method for noninvasive assessment of liver fibrosis.6 – 8 Solid tumors located near the gastrointestinal tract are easily visualized by real-time endoscopic ultrasound (EUS) elastography and potentially characterized by this technique. EUS elastography was used previously in several studies for the characterization and differentiation of benign and malignant lymph nodes, with variable sensitivity, specificity, and accuracy.9 –12 The technique also has been used for the differential diagnosis of focal pancreatic masses, with variable accuracy and reproducibility.13–17 The results of the initial studies using qualitative data based on elastographic patterns seemed to be less reliable and inconsistent.13,14 Other studies yielded high values of accuracy (usually between 80% and 90%), based on a robust quantitative methodology and strict diagnostic criteria,15–17 with the same results also reported when the data were analyzed in a multicentric study design.18,19

Abbreviations used in this paper: ANN, artificial neural network; AUC, area under the receiver operating characteristic curve; CAD, computeraided diagnosis; CI, confidence interval; EUS, endoscopic ultrasound; FNA, fine-needle aspiration; IT, Information Technology; MLP, multilayer perceptron; NPV, negative predictive value; PPV, positive predictive value; SD, standard deviation. © 2012 by the AGA Institute 1542-3565/$36.00 doi:10.1016/j.cgh.2011.09.014

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Figure 1. Chronic pseudotumoral pancreatitis in a 41-year-old man (mixed EUS elastography aspect).

Computer-aided diagnosis (CAD), as a relatively recent interdisciplinary methodology, interprets medical images by computer technology to obtain automatic differential diagnosis of lesions. Quantitative analysis of real-time EUS elastography seemed to yield better results in initial studies, based on dynamic computer analysis of prerecorded image sequences (Supplementary Videos 1 and 2).12 This approach might be devoid of possible selection bias of individual images, perception errors of hue colors, and motion-related artifacts. Another approach within CAD is based on artificial neural networks (ANNs), which previously have been used for the differential diagnosis of pancreatic masses.15 Synthetically speaking, ANNs represent nonalgorithmic adaptive information processing systems, learning from examples and behaving similar to black boxes because the way they process information is inexplicit. Basically, ANNs represent a massively parallel distributed processor, consisting of simple processing units (the artificial neurons), storing experiential knowledge and making it available for use. They can be thought of as an information-processing paradigm, inspired by the way the human brain processes information.20,21

Patients and Methods Patients Thirteen European centers, comprising the European EUS Elastography Multicentric Study Group, participated in the study, which included 258 patients with 774 EUS elastography recordings. The inclusion criteria were as follows: patients diagnosed with solid pancreatic tumor masses, age between 18 and 90 years, and both sexes (men and women) who signed informed consent for EUS with real-time elastography and EUS-guided fine-needle aspiration (FNA) biopsy. All patients agreed that their investigation results would be used in clinical research. Patients who had undergone prior surgical treatment with curative intent or chemoradiotherapy and also patients diagnosed with mucin-producing tumors or pancreatic cystic tumors were excluded from the study. The basic results, including a primary qualitative analysis and interobserver variability, as well as quantitative analysis of average hue histograms and intraobserver variability, were reported in a previous article.19 Based on mean hue histogram values, the method proved to have good reproducibility and good parameters of the receiver-operating-characteristic analysis (sensitivity, specificity,

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positive predictive value [PPV], negative predictive value [NPV], and overall accuracy). The methodology of the multicentric study was described in detail in a previous article using the same patient subgroups, but a completely different statistical analysis approach.19 Briefly, a central dedicated web site was designed to collect the patient demographic data and EUS elastography movies from the active centers. The data collected for each patient were as follows: center/doctor of origin, name initials, birth date, sex, examination date, focal mass characteristics (size, location, echogenicity, echostructure), as well as final diagnosis (obtained by EUS-FNA cytology/microhistology, surgical pathology, and/or clinical follow-up period of a minimum of 6 mo), as described in the previous article.19 Thus, the diagnosis of chronic pancreatitis was based on the clinical information, as well as a combination of imaging methods, with at least 4 criteria of chronic pancreatitis during EUS (including calcifications, hyperechogenic foci, pseudocysts, and honeycomb pattern). The diagnosis of chronic pseudotumoral pancreatitis was always confirmed by surgery or by a follow-up period of at least 6 months, which was used to exclude malignancy in the patients who did not undergo surgery. A positive cytologic diagnosis obtained by EUS-FNA was considered final proof of malignancy of the pancreatic mass. The diagnoses obtained by EUS-FNA also were verified by surgery or clinical follow-up evaluation for at least 6 months, based on clinical examination, biological examinations, and repeat imaging tests (either computerized tomography or EUS). The design of the study was prospective, blinded, and multicentric. Informed consent was used in each center for each patient included, according to local practices, and the study was approved by the Ethical Committee of the University of Medicine and Pharmacy (Craiova, Romania). Moreover, EUS and EUS-FNA are clinical procedures used currently in daily practice, followed by software manipulation of the data needed to obtain elastography movies and subsequent computer algorithms embedded in the ultrasound system.

Endoscopic Ultrasound Elastography EUS elastography was performed during usual EUS examinations (7.5-MHz frequency), with 3 movies lasting 10 seconds each recorded on the embedded hard disk drive of the ultrasound system to minimize variability and to increase re-

Figure 2. Pancreatic adenocarcinoma in a 64-year-old man (hard EUS elastography aspect).

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rows (each row for each frame) and j ⫽ 256 columns (each column for each color). Accordingly, aij represents the frequency of the (hue) color j in the ith frame, and thus the matrix (aij) is a natural mathematic correspondent of an EUS elastography movie.

Neural Computing Approach

Figure 3. Pancreatic adenocarcinoma correctly diagnosed by AAN. Two different frames from the same patient with completely different elastographic appearance: (A) a soft-mixed aspect suggesting chronic pancreatitis, and (B) a hard aspect suggesting pancreatic cancer.

peatability of acquisition (movies 1 and 2, Figures 1 and 2). All movies (3 ⫻ 258 patients, ie, 774 movies) were uploaded to the same central web site, together with patient demographic data, and further processed by the Information Technology (IT) team. A 2-panel image with the usual conventional gray-scale Bmode EUS image on the right side and with the elastography image on the left side was used in each center. The region of interest for EUS elastography was preferably larger than the focal pancreatic mass, to include the surrounding structures with a ratio of approximately 50%. For large tumors (⬎3 cm) only part of the tumor was included in the elastography region of interest, with approximately 50% located on the tumor and 50% on surrounding tissues. The following settings for the EUS elastography software were used uniformly in all the active centers (1/-/-/2/3/4 T-Elasto-H), that is, reject function 1, e-smoothing 2, persistence 3, and e-dynamic range 4.

Postprocessing Endoscopic Ultrasound Elastography Movies To minimize the possible human selection bias of individual images and perception errors of hue colors (Figure 3), all the postprocessing and computer analysis of digital EUS elastography movies was performed within the IT Center in Craiova, with programmers and statisticians being blinded to the clinical, pathologic, and imaging data. A special plug-in written by the IT team (based on ImageJ software, National Institutes of Health, Bethesda, MD) was used to analyze individual frames of EUS elastography movies by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. Because the corresponding EUS elastography sample movie consists of a sequence of 125 frames (static images) displaying 256 colors, a corresponding number of 125 hue histograms are obtained, providing the distributions of colors (hues) in each frame. All the postprocessing analysis was performed by the IT team. Only the manual selection of the tumor area in the ImageJ program was performed by 2 experienced doctors (A.S. and D.I.G.). The same doctors recalled all the movies of the patients and indicated a diagnosis only considering the qualitative elastographic appearance. From a mathematic point of view, each EUS elastography movie of a patient corresponds to a matrix (aij), with i ⫽ 125

The natural mathematic input of ANNs is represented by vectors rather than matrices. The vector represents the average (hue) vector synthesizing the information provided by the EUS elastography movie in an appropriate input shape. Starting from the database containing the hue vectors of the scanned movies of different focal pancreatic masses, together with the corresponding final diagnosis previously established, ANNs are trained to learn to associate a certain hue vector with 256 features (components) to the corresponding diagnosis (benign/malignant) class, which is the 257th component representing the class label. Among standard ANNs, the multilayer perceptron (MLP) is perhaps the most popular network architecture in use today. Basically, each computation unit performs a biased weighted sum of its input and passes this activation level through an activation function to produce its output. All the artificial neurons are arranged in a layered feed-forward topology. Such networks can model functions of almost arbitrary complexity, MLPs being applied successfully to solve difficult and diverse problems.20,21 A key observation in the practical use of MLP is that MLP with only 2 hidden layers is theoretically sufficient to model almost any real-life problem. This fact is based on the universal approximation theorem for nonlinear input-output mapping, which is directly applicable to MLPs. It is worth mentioning in this context that increasing the number of hidden units increases the modeling power of an ANN, but also makes it larger, more difficult to train, slower to operate, and, consequently, more prone to overlearning. During the automatic diagnosis process, a MLP with 2 hidden layers trained with the backpropagation algorithm has been used. We experimentally performed 100 training computer runs with different numbers of hidden units in each layer, ranging from 1 to 128, and we eventually chose 55 U in the first layer and 34 in the second layer as the optimal number, providing the best accuracy in the experiment (Supplementary Figure 1). Moreover, after some trial and error, we chose the constant learning rate ␩ of 0.01/momentum of 0.3, although this is not necessarily the optimal choice. Because different MLP computer runs provide different outcomes, it is necessary to evaluate the overall decision performance of such an approach, obtained in a sufficiently large number of experiments, aiming to improve the reliability of the experimental results. A solution to this problem, with both theoretic and practical potential, involves statistical means. Thus, using statistical measures of the performance of binary classification techniques, applied to assess the decision accuracy obtained by different ANNs experiments, should bring the benefits of improving the reliability and credibility of the findings thus obtained. Consequently, a priori statistical power analysis in conjunction with the corresponding sample size estimation (2-tailed type of null hypothesis) has been performed previously to establish the minimum number of independent computer runs needed to provide accurate and reliable judgments. Accordingly, a sample of 100 different computer

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EUS ELASTOGRAPHY IN FOCAL PANCREATIC MASSES

Table 1. Descriptive Statistics for Included Patients

Sex, n Male (n ⫽ 172) Female (n ⫽ 86) Age, y Mean (⫾SD) Range Tumor Size on EUS, mm Mean (⫾SD) Range Localization Head (⫹uncinate) Body Tail FNA, yes/no

Pancreatic cancer

Chronic pancreatitis

139 72

33 14

64 (15.40) 18–89

56 (13.25) 18–81

31.97 (11.69) 6–85

28.36 (12.23) 9–60

153 (4) 38 16 181/30

36 (2) 6 3 34/13

runs of the ANN model has been considered, providing a statistical power of 95% (for type I error ␣ equaling 0.05) for the statistical comparison tests with other decision models foreseen to be performed in future works, and also providing an almost normal distribution of the results. Next, the common 10-fold cross-validation has been used to assess the neural network performance, as described in detail in a previous pilot study.15 As we stated earlier, to statistically validate the medical decision making by ANN means, we ran the algorithm 100 times in a complete cross-validation cycle, to avoid the inherent bias induced by its heuristic nature. Accordingly, the results of the diagnosing performance of the MLP model have been reported in terms of mean (seen as the diagnosis likelihood), standard deviation (SD), and 95% confidence interval (CI), averaged over the 100 different computer runs of a complete cross-validation cycle. To provide more insight into the classification process, we also have provided both the corresponding confusion matrix and 4 important classification parameters: sensitivity, specificity, PPV, and NPV.

Results The cases included 172 men and 76 women with mean age (⫾SD) of 64 years (⫾15.40 y) for pancreatic cancer patients and 56 years (⫾13.25 y) for chronic pancreatitis patients (Table 1). The sensitivity and specificity of the EUS elastography readings obtained by 2 independent doctors observing only the encoded recordings were as follows: 84.4% and 46.8% for the first reader, and 75.4% and 53.2% for the second reader, respectively. The results of the automatic diagnosis performance in terms of mean, SD, and 95% CI, averaged over the 100 different computer runs of a complete cross-validation cycle, are displayed in Table 2. Although the calculation of CIs is based on

Table 2. MLP Diagnostic Performances Mean (⫾SD) training performance, %

Mean (⫾SD) testing performance, %

95% CI training, %

95% CI testing, %

91.14 (6.4)

84.27 (5.90)

(89.87–92.42)

(83.09–85.44)

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Table 3. Confusion Matrix Observed classes Pancreatic cancer (⫹)

Predicted classes

Pancreatic cancer (⫹) 565 (True positive) Chronic pancreatitis (⫺) 80 (False negative)

Chronic pancreatitis (⫺) 22 (False positive) 107 (True negative)

the assumption that the variable is distributed normally, in our case this assumption is practically fulfilled because the sample size is 100. The 95% CI was calculated using the formula given by mean ⫾ 1.96 ⫻ SD/公n. The confusion matrix, corresponding to the average MLP performance over 100 different computer runs of complete cross-validation cycles, is displayed in Table 3. The obtained corresponding sensitivity, specificity, PPV, and NPV values were 87.59%, 82.94%, 96.25%, and 57.22%, respectively. The neural computing approach provided 91.14% (95% CI, 89.87%–92.42%) training accuracy and 84.27% (95% CI, 83.09%– 85.44%) testing accuracy. The corresponding average area under the receiver operating characteristic curve (AUC), over the 100 different computer runs of a complete crossvalidation cycle, was 0.94 with a 95% CI of 0.91 to 0.97. Recall that in the case of a 2-class decision problem, the ROC curve summarizes the performance of the classifier across the range of possible thresholds, plotting the sensitivity versus 1 minus the specificity. Practically, instead of plotting the corresponding ROC curve, one calculates AUC, bearing in mind that higher AUC value implies better classification performance (maximum AUC ⫽ 1). The summary statistics of the automatic diagnosis process are provided in Table 4, showing both the number/ proportion of correct/wrong classified patients, and unclassified patients, for each decision class.

Discussion The differential diagnosis of pancreatic cancer and chronic pseudotumoral pancreatitis is still difficult and it can be a challenge for the expert gastroenterologist. Age younger than 50 years, male sex, black race, and the absence of jaundice can be considered predictors for chronic pancreatitis.22 In the presence of a focal mass, the sensitivity and accuracy of EUSFNA are variable, usually approximately 80% to 90% in the reported studies, with lower values (50%–75%) reported in patients with chronic pancreatitis.23 Although the PPV of EUSFNA approaches 100%, the NPV of EUS-FNA is still low, although pancreatic cancer cannot be excluded reliably in this subgroup of patients. Suspected reasons for this situation are considered as follows: co-existing pancreatitis, technical diffiTable 4. Summary Statistics

Total Correct Wrong Unclassified Correct, % Wrong, % Unclassified, %

Pancreatic cancer

Chronic pancreatitis

645 565 80 0.00 87.59 12.40 0.00

129 107 22 0.00 82.95 17.05 0.00

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culty owing to scope positioning in uncinate lesion, difficult cytology, presence of ascites or collaterals, pathologist’s interobserver variation, and so forth.24,25 Repeat EUS-FNA in patients with high clinical suspicion for pancreatic cancer can be a safe and reliable solution.25 EUS elastography imaging offers complementary information added to conventional EUS with minimal prolongation of the examination time, minimum costs, and no added morbidity or mortality. Two-dimensional digital images, such as EUS elastography images, are composed of color pixels, which are basic finite elements of a digital image. The arrangement of these pixels reflects the structure and texture of the object that has been imaged.26 For elastography (strain) imaging, the color pictures displayed reflect the differences in the strain (displacement) values obtained during slight compression induced by respiratory/heart movements, or directly by the EUS transducer,1 being an indirect measure of the elastic structure of the focal masses. Initial articles based on analysis of the EUS elastography images or movies yielded variable results for the differential diagnosis of focal pancreatic masses.13–18 Also, in the initial results of the multicentric data performed by mean hue histogram values averaged more than 10 seconds, the ROC analysis yielded an AUROC of 0.854, with an overall accuracy between 79.1% and 90.7% as a function of the chosen cut-off value necessary for the differential diagnosis of pseudotumoral chronic pancreatitis and pancreatic cancer.19 Based on a previously established cut-off value of 175 for the mean hue histogram values, the sensitivity, specificity, PPV, and NPV were 93.36%, 65.96%, 92.5%, and 68.9%, respectively, with an overall accuracy of 85.36%. Furthermore, the direct visual analysis of elastography recorded movies offered unsatisfactory results concerning sensitivity and specificity, for 2 different observers blinded to clinical data (84.4% and 46.8% for the first observer, as well as 75.4% and 53.2% for the second observer, respectively). Consequently, we decided to perform a MLP-based ANN analysis of the same database described previously to verify if the overall accuracy of the diagnosis can be improved. Recently, CAD has become a part of the routine clinical work for detection of cancers at many screening sites and hospitals27 in the United States and Western Europe. Although computers are indispensable for the analysis of large amounts of data involved in any image analytic technique, the human cortex remains the most complex image analysis tool.23 Considering this, ANNs, as intelligent systems inspired by the human perception models, must be considered as a natural component of the image analytic tools. In this study, the effectiveness of a MLP–neural network approach was investigated for the task of providing a reliable real-time decision support for differential diagnosis of pancreatic cancer and chronic pancreatitis. Future works will include the use of other types of ANNs (eg, radial basis function, probabilistic neural networks), committees of ANNs, or natural computing algorithms such as the support vector machines, and so forth, known for their effectiveness when applied to medical databases. The main strength of the current multicentric study relative to other published data are the inclusion of a large group of patients and doctors performing EUS elastography. The data were gathered prospectively through a central web site, although all the statistical and computer analyses were performed in the IT department of the central site, blinded to the clinical and pathologic information. However, several limitations of the study were described in our

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previous article, including the unbalanced distribution of pseudotumoral chronic pancreatitis (n ⫽ 47) and pancreatic cancer (n ⫽ 211) patients.19 Recall that the first published “self-teaching” neural network program intended to differentiate chronic pancreatitis and pancreatic adenocarcinoma used a simplified version of image analysis based on single EUS gray-scale images.28 This feasibility study was based on a small number of patients with chronic pancreatitis (n ⫽ 14) and pancreatic cancer (n ⫽ 21), but the accuracy was similar, being 80% for computer analysis, 83% for blinded videotape assessment, and 85% for real-time evaluation of EUS images. Improved methodology could be designed through analysis of a large number of texture parameters of gray-scale EUS movies, which was possible because of recent advances in software and hardware.23 However, the study included a small number of patients with normal pancreas (n ⫽ 22), chronic pancreatitis (n ⫽ 12), and pancreatic cancer (n ⫽ 22), with promising results of the ANN analysis, consisting of an AUROC of 0.93. We also published a small pilot study concerning the value of ANN analysis for the differential diagnoses of focal pancreatic masses, including 43 cases with pancreatic adenocarcinoma (n ⫽ 32) and chronic pseudotumoral pancreatitis (n ⫽ 11).15 The ANN model used had a training performance of 97% on average, together with a testing performance of 90% on average, with low corresponding SDs of 3.2% and 12.31%, respectively. This indicated a high stability of the model, whereas the area under the ROC curve (0.965) indicates a very good classification performance. The diagnosis accuracy of the MLP model designed in the current EUS elastography multicentric study was in accordance with reported standard EUS elastography experience,13–19 nevertheless, with an improved robustness, reliability, and reproducibility. It is worth emphasizing that the ANN approach provides a very fast and accurate diagnosis for individual cases, supporting and improving real-time human decision making. Because there was no significant difference between the training (91.14%) and the testing performance (84.27%), we can conclude that the neural model cross-validates well, providing a good generalization power, reaching a reliable diagnosis for new cases. Furthermore, the inspection of the standard deviation, in conjunction with the confidence interval size (relatively small SD values and CI sizes) shows that MLP had balanced behavior and avoided overfitting. Based on this single ANN approach backed by the MLP model, chronic pancreatitis has been classified correctly in 87.59% of cases, whereas pancreatic cancer has been classified correctly in 82.95% cases. Moreover, the corresponding AUROC for the ANN classification process was 0.94, which was significantly higher than the values obtained by simple mean hue histogram analysis in which the AUROC was 0.85. This indicates a significant advantage over the previously obtained data analysis of the multicentric study, in which the overall accuracy was variable as a function of the established cut-off value.19 Also, set-up of an ANN model-based CAD analysis can indicate an automated diagnosis in individual cases, with the obvious advantage of learning from previous experience. This is different from the earlier-described approach, in which a unique ANN model was used successfully. Further work has to investigate the effectiveness of a natural computing system, integrating a certain number of high-performance ANNs in a compet-

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itive/collaborative way, to obtain a global automatic medical diagnosis. Thus, such a competitive/collaborative decision model will represent a dynamic CAD system, characterized by a strong adaptability to each particular case to be solved.29 We have already tested this approach in a limited number of patients (n ⫽ 68) from a single center and proved that it might significantly improve the diagnostic performance as compared with application of stand-alone ANN analysis. As previously suggested, this complex ANN-based computing system might be naturally widened by including other trained intelligent tools in its structure, such as support vector machines, Bayesian classifiers, k-nearest neighbors, and so forth. In conclusion, integration of clinical data into efficacious ANNs, in concordance with imaging enhancements (real-time sono-elastography, contrast-enhancement, hybrid imaging, 3-dimensional imaging, and so forth) and cytologic parameters, would certainly be beneficial for improved clinical decision making in patients with focal pancreatic lesions. This is highly important in the subgroup of patients with negative EUS-FNA results for whom a decision should be made regarding a repeat EUS-FNA, long-term follow-up evaluation, or immediate referral to surgery. Although it is now obvious that EUS elastography will certainly not replace EUS-FNA, the introduction of real-time CAD systems based on previous training/testing of ANNs for imaging analysis will certainly benefit individual decisions in patients with unclear diagnosis, thus supporting enhanced clinical decision-making algorithms.

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Supplementary Material Note: To access the supplementary material accompanying this article, visit the online version of Clinical Gastroenterology and Hepatology at www.cghjournal.org, and at doi:10.1016/ j.cgh.2011.09.014.

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Reprint requests Address requests for reprints to: Dan Ionut᝺ Gheonea, MD, PhD, MSc, Gastroenterology Department, Research Center of Gastroenterology and Hepatology, University of Medicine and Pharmacy, Craiova, Petru

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Rares¸, No. 2, Craiova, Dolj, 200349, Romania. e-mail: digheonea@ gmail.com; fax: (40) 251-593077. Acknowledgments A different article using a completely different statistical analysis, but the same patient subgroups, was published in Endoscopy Journal (2011;43:596 – 603; PMID: 21437851). ClinicalTrials.gov identifier: NCT00909103. Responsible party: University of Medicine and Pharmacy Craiova, Romania; study ID numbers: EUS-EG001, EUS-EG-UMFCV-RO. The authors thank the study group collaborators Ca ˘ta ˘lin Manea, Alexandru Iordache, and Gabriel Lucian Popescu (Gastroenterology Department, University of Medicine and Pharmacy, Craiova, Romania); Hazem Hassan (Department of Surgical Gastroenterology, Gentofte and Herlev Hospital, University of Copenhagen, Denmark); Claus W. Fristrup and Michael B. Mortensen (Center for Surgical Ultrasound, Department of Surgery, Odense University Hospital, Odense Denmark); Jose Lariño-Noia and Juan Enrique Dominguez-Munoz (Gastroenterology, University Hospital, Santiago de Compostela, Spain); Silvia Carrara and Maria Chiara Petrone (Gastroenterology and Gastrointestinal

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Endoscopy Unit, Vita Salute San Raffaele University, Milan, Italy); Erwan Bories (Endoscopic Unit, Paoli-Calmettes Institut, Marseilles, France); Andre Ignee (Med Klinik 2, Caritas-Krankenhaus Bad Mergentheim, Germany); Svein Odegaard and Lars Birger Nesje (Institute of Medicine, University of Bergen and National Centre for Ultrasound in Gastroenterology, Haukeland University Hospital, Bergen, Norway); Mihai Ciocârlan and Liana Gheorghe (Fundeni Clinical Institute of Digestive Diseases and Liver Transplantation, Bucharest Romania); and Smaranda Belciug, Marina Gorunescu, Ruxandra Stoean, and Ca ˘ta ˘lin Stoean (Department of Computer Science, Faculty of Mathematics and Computer Science, University of Craiova, Romania). Conflicts of interest The authors disclose no conflicts. Funding This study was partially supported by a PANGEN project financed by the Ministry of Education and Research, Romania (contract number 42-110/2008).

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Supplementary Figure 1. The scheme of the ANN with 2 hidden layers used in the study for the differential diagnosis between chronic pseudotumoral pancreatitis and pancreatic cancer.

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