用CT影像和报告融合模型预测胰腺癌五年生存风险
Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports
- 融合放射科报告与CT影像的深度学习模型
- 内部/外部数据集C-index达0.6750和0.6435
- 可区分高低风险组，适合早期筛查研究
胰腺导管腺癌（PDAC）是一种高度侵袭性癌症，多数病例在Ⅳ期确诊，五年总生存率低于5%。早期检测与预后建模对改善患者预后、指导早期干预至关重要。本研究开发并评估了一种结合放射科报告与CT影像的深度学习融合模型，用于预测PDAC风险。该模型在内部与外部数据集上对五年生存风险估计的协和指数（C-index）分别为0.6750（95% CI: 0.6429, 0.7121）和0.6435（95% CI: 0.6055, 0.6789）。Kaplan-Meier分析显示，模型预测的低危与高危组间存在显著差异（p<0.0001）。结果表明，基于深度学习的生存模型能有效利用临床与影像数据，为胰腺癌早期预警提供潜在工具。
原文摘要 · Abstract (English)
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer, with most cases diagnosed at stage IV and a five-year overall survival rate below 5%. Early detection and prognosis modeling are crucial for improving patient outcomes and guiding early intervention strategies. In this study, we developed and evaluated a deep learning fusion model that integrates radiology reports and CT imaging to predict PDAC risk. The model achieved a concordance index (C-index) of 0.6750 (95% CI: 0.6429, 0.7121) and 0.6435 (95% CI: 0.6055, 0.6789) on the internal and external dataset, respectively, for 5-year survival risk estimation. Kaplan-Meier analysis demonstrated significant separation (p<0.0001) between the low and high risk groups predicted by the fusion model. These findings highlight the potential of deep learning-based survival models in leveraging clinical and imaging data for pancreatic cancer.
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