Department of Radiology, The First Affiliated Hospital of Jinan University, No. 613 Huangpu West Road, Tianhe District, Guangzhou, 510627, Guangdong, China.
Shenzhen Research Institute of Big Data, Shenzhen, Guangdong, China.
Cancer Imaging. 2022 May 12;22(1):23. doi: 10.1186/s40644-022-00457-3.
BACKGROUND: Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach. METHODS: This retrospective cohort study included a total of 605 patients with intermediate-stage HCC who received TACE as their initial therapy. A fully automated framework (i.e., DSA-Net) contained a U-net model for automatic tumor segmentation (Model 1) and a ResNet model for the prediction of treatment response to the first TACE (Model 2). The two models were trained in 360 patients, internally validated in 124 patients, and externally validated in 121 patients. Dice coefficient and receiver operating characteristic curves were used to evaluate the performance of Models 1 and 2, respectively. RESULTS: Model 1 yielded a Dice coefficient of 0.75 (95% confidence interval [CI]: 0.73-0.78) and 0.73 (95% CI: 0.71-0.75) for the internal validation and external validation cohorts, respectively. Integrating the DSA videos, segmentation results, and clinical variables (mainly demographics and liver function parameters), Model 2 predicted treatment response to first TACE with an accuracy of 78.2% (95%CI: 74.2-82.3), sensitivity of 77.6% (95%CI: 70.7-84.0), and specificity of 78.7% (95%CI: 72.9-84.1) for the internal validation cohort, and accuracy of 75.1% (95% CI: 73.1-81.7), sensitivity of 50.5% (95%CI: 40.0-61.5), and specificity of 83.5% (95%CI: 79.2-87.7) for the external validation cohort. Kaplan-Meier curves showed a significant difference in progression-free survival between the responders and non-responders divided by Model 2 (p = 0.002). CONCLUSIONS: Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.
背景：经导管动脉化疗栓塞术（TACE）是治疗中期肝细胞癌（HCC）的主要方法；然而，其疗效在同一肿瘤分期的患者之间存在差异。准确预测 TACE 反应仍然是避免过度治疗的主要关注点。因此，我们旨在开发和验证一种基于深度学习方法的人工智能系统，通过数字减影血管造影（DSA）视频实时自动预测 HCC 患者的 TACE 反应。
方法：本回顾性队列研究共纳入 605 例接受 TACE 作为初始治疗的中期 HCC 患者。一个完全自动化的框架（即 DSA-Net）包含一个用于自动肿瘤分割的 U-net 模型（模型 1）和一个用于预测首次 TACE 治疗反应的 ResNet 模型（模型 2）。两个模型在 360 名患者中进行训练，在 124 名患者中进行内部验证，在 121 名患者中进行外部验证。使用 Dice 系数和受试者工作特征曲线分别评估模型 1 和模型 2 的性能。
结果：模型 1 在内部验证队列和外部验证队列中的 Dice 系数分别为 0.75（95%置信区间 [CI]：0.73-0.78）和 0.73（95%CI：0.71-0.75）。整合 DSA 视频、分割结果和临床变量（主要是人口统计学和肝功能参数）后，模型 2 对首次 TACE 治疗反应的预测准确率为 78.2%（95%CI：74.2-82.3），灵敏度为 77.6%（95%CI：70.7-84.0），特异性为 78.7%（95%CI：72.9-84.1）内部验证队列，准确率为 75.1%（95% CI：73.1-81.7），灵敏度为 50.5%（95%CI：40.0-61.5），特异性为 83.5%（95%CI：79.2-87.7）外部验证队列。Kaplan-Meier 曲线显示，根据模型 2 划分的无进展生存期在反应者和无反应者之间存在显著差异（p=0.002）。
结论：我们的多任务深度学习框架为解码 DSA 视频提供了一种实时有效的方法，可以为现实环境中中期 HCC 患者的 TACE 治疗提供临床决策支持。
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