Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.

Autor: Qureshi TA; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Gaddam S; Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Wachsman AM; Department of Radiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Wang L; Department of Radiology, Chaoyang Hospital, Beijing, China., Azab L; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Asadpour V; Southern California Kaiser Permanente Medical Center, Los Angeles, CA, USA., Chen W; Southern California Kaiser Permanente Medical Center, Los Angeles, CA, USA., Xie Y; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Wu B; Southern California Kaiser Permanente Medical Center, Los Angeles, CA, USA., Pandol SJ; Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA., Li D; Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jazyk: angličtina
Zdroj: Cancer biomarkers : section A of Disease markers [Cancer Biomark] 2022; Vol. 33 (2), pp. 211-217.
DOI: 10.3233/CBM-210273
Abstrakt: 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.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje