使用CT影像和放射学报告对胰腺癌进行机会性筛查
Opportunistic Screening for Pancreatic Cancer using Computed Tomography Imaging and Radiology Reports
AI总结：
本研究开发了一个融合放射学报告和CT影像的深度学习模型，用于预测胰腺导管腺癌风险，在内外部数据集上实现了有效的5年生存风险估计，显著区分了高低风险组。
摘要：
胰腺导管腺癌（PDAC）是一种高度侵袭性的癌症，大多数病例在IV期确诊，五年总生存率低于5%。早期检测和预后建模对于改善患者结局和指导早期干预策略至关重要。在本研究中，我们开发并评估了一个深度学习融合模型，该模型整合了放射学报告和CT影像来预测PDAC风险。该模型在内部和外部数据集上进行5年生存风险估计时，一致性指数（C-index）分别达到0.6750（95% CI: 0.6429, 0.7121）和0.6435（95% CI: 0.6055, 0.6789）。Kaplan-Meier分析表明，融合模型预测的低风险组和高风险组之间存在显著分离（p<0.0001）。这些发现突显了基于深度学习的生存模型在利用临床和影像数据进行胰腺癌预测方面的潜力。
英文摘要：
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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