Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Gastroenterology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Cancer Biomark. 2022;33(2):211-217. doi: 10.3233/CBM-210273.
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.
由于缺乏特异性诊断生物标志物，早期诊断胰腺导管腺癌（PDAC）具有挑战性。然而，对 PDAC 高危个体进行分层，然后定期监测其健康状况，有可能在早期进行诊断。
通过识别术前腹部计算机断层扫描（CT）中预测 PDAC 的特征，对 PDAC 高危个体进行分层。
在对 4000 个从术前胰腺提取的原始放射组学参数的分析中，确定了一组可能预测 PDAC 的 CT 特征。然后，使用朴素贝叶斯分类器对胰腺 CT 扫描进行自动分类，这些 CT 扫描具有 PDAC 的高风险。本研究使用了来自 72 名受试者的 108 套回顾性 CT 扫描（每组 36 套，分别来自健康对照组、术前组和诊断组）。模型开发在 66 个多期 CT 扫描上进行，而外部验证在 42 个静脉期 CT 扫描上进行。
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、量化和解释术前胰腺的微观变化，并能有效地辅助 PDAC 高危个体的分层。
