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

Autor:	Javed S; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Qureshi TA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Gaddam S; Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Wang L; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Azab L; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Wachsman AM; Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Chen W; Department of Research and Evaluation, Southern California Kaiser Permanente Medical Center, Los Angeles, CA, United States., Asadpour V; Department of Research and Evaluation, Southern California Kaiser Permanente Medical Center, Los Angeles, CA, United States., Jeon CY; Division of Hematology, Cedars-Sinai Medical Center, Los Angeles, CA, United States.; Division of Oncology, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Wu B; Department of Research and Evaluation, Southern California Kaiser Permanente Medical Center, Los Angeles, CA, United States., Xie Y; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Pandol SJ; Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, United States., Li D; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in oncology [Front Oncol] 2022 Nov 09; Vol. 12, pp. 1007990. Date of Electronic Publication: 2022 Nov 09 (Print Publication: 2022).
DOI:	10.3389/fonc.2022.1007990
Abstrakt:	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. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions 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 subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Javed, Qureshi, Gaddam, Wang, Azab, Wachsman, Chen, Asadpour, Jeon, Wu, Xie, Pandol and Li.)
Databáze:	MEDLINE
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