BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.
BACKGROUND: Early stage diagnosis of Pancreatic Ductal Adenocarcinoma (PDAC) is challenging due to the lack of specific diagnostic biomarkers. However, stratifying individuals at high risk of PDAC, followed by monitoring their health conditions on regular basis, has the potential to allow diagnosis at early stages. OBJECTIVE: To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. METHODS: A set of CT features, potentially predictive of PDAC, was identified in the analysis of 4000 raw radiomic parameters extracted from pancreases in pre-diagnostic scans. The naïve Bayes classifier was then developed for automatic classification of CT scans of the pancreas with high risk for PDAC. A set of 108 retrospective CT scans (36 scans from each healthy control, pre-diagnostic, and diagnostic group) from 72 subjects was used for the study. Model development was performed on 66 multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans. RESULTS: The system achieved an average classification accuracy of 86% on the external dataset. CONCLUSIONS: Radiomic analysis of abdominal CT scans can unveil, quantify, and interpret micro-level changes in the pre-diagnostic pancreas and can efficiently assist in the stratification of high risk individuals for PDAC.
Authors: Jorg Kleeff; Murray Korc; Minoti Apte; Carlo La Vecchia; Colin D Johnson; Andrew V Biankin; Rachel E Neale; Margaret Tempero; David A Tuveson; Ralph H Hruban; John P Neoptolemos Journal: Nat Rev Dis Primers Date: 2016-04-21 Impact factor: 52.329
Authors: Alison P Klein; Sara Lindström; Julie B Mendelsohn; Emily Steplowski; Alan A Arslan; H Bas Bueno-de-Mesquita; Charles S Fuchs; Steven Gallinger; Myron Gross; Kathy Helzlsouer; Elizabeth A Holly; Eric J Jacobs; Andrea Lacroix; Donghui Li; Margaret T Mandelson; Sara H Olson; Gloria M Petersen; Harvey A Risch; Rachael Z Stolzenberg-Solomon; Wei Zheng; Laufey Amundadottir; Demetrius Albanes; Naomi E Allen; William R Bamlet; Marie-Christine Boutron-Ruault; Julie E Buring; Paige M Bracci; Federico Canzian; Sandra Clipp; Michelle Cotterchio; Eric J Duell; Joanne Elena; J Michael Gaziano; Edward L Giovannucci; Michael Goggins; Göran Hallmans; Manal Hassan; Amy Hutchinson; David J Hunter; Charles Kooperberg; Robert C Kurtz; Simin Liu; Kim Overvad; Domenico Palli; Alpa V Patel; Kari G Rabe; Xiao-Ou Shu; Nadia Slimani; Geoffrey S Tobias; Dimitrios Trichopoulos; Stephen K Van Den Eeden; Paolo Vineis; Jarmo Virtamo; Jean Wactawski-Wende; Brian M Wolpin; Herbert Yu; Kai Yu; Anne Zeleniuch-Jacquotte; Stephen J Chanock; Robert N Hoover; Patricia Hartge; Peter Kraft Journal: PLoS One Date: 2013-09-13 Impact factor: 3.240
Authors: Wazir Muhammad; Gregory R Hart; Bradley Nartowt; James J Farrell; Kimberly Johung; Ying Liang; Jun Deng Journal: Front Artif Intell Date: 2019-05-03
Authors: Brian M Nolen; Randall E Brand; Denise Prosser; Liudmila Velikokhatnaya; Peter J Allen; Herbert J Zeh; William E Grizzle; Ying Huang; Aleksey Lomakin; Anna E Lokshin Journal: PLoS One Date: 2014-04-18 Impact factor: 3.240