AUTHOR=Javed Sehrish , Qureshi Touseef Ahmad , Gaddam Srinivas , Wang Lixia , Azab Linda , Wachsman Ashley Max , Chen Wansu , Asadpour Vahid , Jeon Christie Younghae , Wu Beichien , Xie Yibin , Pandol Stephen Jacob , Li Debiao 

TITLE=Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.1007990

DOI=10.3389/fonc.2022.1007990

ISSN=2234-943X

ABSTRACT=Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Identifying such changes can efficiently assist in the risk stratification of PDAC. 
In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic sub-regions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic sub-regions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the sub-region(s) likely to develop a tumor. 
Model training was performed on multiphase CT scans, whereas external validation was performed on 42 venous-phase CT scans where ~89.3% classification accuracy was achieved on average, with sensitivity (true positive rate) and specificity (true negative rate) reaching 85.7% and 92.8%, respectively, for predicting development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic sub-regions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.