Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.
Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images.
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DOI：
10.3233/cbm-210273
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发表时间：
2022
影响因子：
3.1
通讯作者：
Li, Debiao
中科院分区：
文献类型：
--
作者：
Qureshi, Touseef Ahmad;Gaddam, Srinivas;Wachsman, Ashley Max;Wang, Lixia;Azab, Linda;Asadpour, Vahid;Chen, Wansu;Xie, Yibin;Wu, Bechien;Pandol, Stephen Jacob;Li, Debiao
关键词：
由于缺乏特异性诊断生物标志物，胰腺导管腺癌（PDAC）的早期诊断具有挑战性。然而，对PDAC高风险个体进行分层，然后定期监测他们的健康状况，有可能在早期阶段进行诊断。通过在诊断前腹部计算机断层扫描（CT）中识别预测特征，对PDAC的高风险个体进行分层。一组CT特征，可能预测PDAC，在分析4000个原始放射组学参数提取胰腺在诊断前扫描。然后开发朴素贝叶斯分类器，用于对具有PDAC高风险的胰腺CT扫描进行自动分类。本研究使用了来自72名受试者的一组108次回顾性CT扫描（健康对照组、诊断前组和诊断组各36次扫描）。在66个多相CT扫描上进行模型开发，而在42个静脉相CT扫描上进行外部验证。该系统在外部数据集上的平均分类准确率为86%。腹部CT扫描的放射组学分析可以揭示、量化和解释诊断前胰腺的微观变化，并可以有效地帮助对PDAC高风险个体进行分层。
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. To stratify high risk individuals for PDAC by identifying predictive features in pre-diagnostic abdominal Computed Tomography (CT) scans. 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. The system achieved an average classification accuracy of 86% on the external dataset. 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.