BACKGROUND AND AIMS: Computer-aided diagnosis of EUS images was quite useful in differentiating pancreatic cancer from normal tissue and chronic pancreatitis. This study investigated the feasibility of using computer-aided diagnostic techniques to extract EUS image parameters to distinguish autoimmune pancreatitis from chronic pancreatitis. METHODS: A new descriptor, local ternary pattern variance, was introduced to improve the performance of the classification model. Patients with autoimmune pancreatitis (n = 81) or chronic pancreatitis (n = 100) were recruited for this study. Representative EUS images were selected, and 115 parameters from 10 categories were extracted from the region of interest. Distance-between-class and sequential forward selection algorithms were used for their ideal combination of features that allowed a support vector machine predictive model to be built, trained, and validated. The accuracy, sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) were used to evaluate the performance of experimental results. RESULTS: Fourteen parameters from 3 categories were selected as an ideal combination of features. The sample set was randomly divided into a training set and a testing set by using two different algorithms-the leave-one-out algorithm and the half-and-half method. The half-and-half method yielded an average (± standard deviation) accuracy of 89.3 ± 2.7%, sensitivity of 84.1 ± 6.4%, specificity of 92.5 ± 3.3%, PPV of 91.6 ± 3.7%, and NPV of autoimmune pancreatitis of 88.0 ± 4.1%. CONCLUSIONS: This study shows that, with the local ternary pattern variance textural feature, computer-aided diagnosis of EUS imaging may be valuable to differentiate autoimmune pancreatitis from chronic pancreatitis. Further refinement of such models could generate tools for the clinical diagnosis of autoimmune pancreatitis.
BACKGROUND AND AIMS: Computer-aided diagnosis of EUS images was quite useful in differentiating pancreatic cancer from normal tissue and chronic pancreatitis. This study investigated the feasibility of using computer-aided diagnostic techniques to extract EUS image parameters to distinguish autoimmune pancreatitis from chronic pancreatitis. METHODS: A new descriptor, local ternary pattern variance, was introduced to improve the performance of the classification model. Patients with autoimmune pancreatitis (n = 81) or chronic pancreatitis (n = 100) were recruited for this study. Representative EUS images were selected, and 115 parameters from 10 categories were extracted from the region of interest. Distance-between-class and sequential forward selection algorithms were used for their ideal combination of features that allowed a support vector machine predictive model to be built, trained, and validated. The accuracy, sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) were used to evaluate the performance of experimental results. RESULTS: Fourteen parameters from 3 categories were selected as an ideal combination of features. The sample set was randomly divided into a training set and a testing set by using two different algorithms-the leave-one-out algorithm and the half-and-half method. The half-and-half method yielded an average (± standard deviation) accuracy of 89.3 ± 2.7%, sensitivity of 84.1 ± 6.4%, specificity of 92.5 ± 3.3%, PPV of 91.6 ± 3.7%, and NPV of autoimmune pancreatitis of 88.0 ± 4.1%. CONCLUSIONS: This study shows that, with the local ternary pattern variance textural feature, computer-aided diagnosis of EUS imaging may be valuable to differentiate autoimmune pancreatitis from chronic pancreatitis. Further refinement of such models could generate tools for the clinical diagnosis of autoimmune pancreatitis.
Authors: Linda S Lee; Dana K Andersen; Reiko Ashida; William R Brugge; Mimi I Canto; Kenneth J Chang; Suresh T Chari; John DeWitt; Joo Ha Hwang; Mouen A Khashab; Kang Kim; Michael J Levy; Kevin McGrath; Walter G Park; Aatur Singhi; Tyler Stevens; Christopher C Thompson; Mark D Topazian; Michael B Wallace; Sachin Wani; Irving Waxman; Dhiraj Yadav; Vikesh K Singh Journal: Pancreas Date: 2017 Nov/Dec Impact factor: 3.327
Authors: Piotr Hogendorf; Aleksander Skulimowski; Adam Durczyński; Anna Kumor; Grażyna Poznańska; Aleksandra Oleśna; Joanna Rut; Janusz Strzelczyk Journal: Dis Markers Date: 2017-03-05 Impact factor: 3.434
Authors: Rahul Pannala; Kumar Krishnan; Joshua Melson; Mansour A Parsi; Allison R Schulman; Shelby Sullivan; Guru Trikudanathan; Arvind J Trindade; Rabindra R Watson; John T Maple; David R Lichtenstein Journal: VideoGIE Date: 2020-11-09