Chen Jialin, Chen Juan, Ye Yamei, Lu Linbin, Guo Xinying, Gao Simiao, Liu Lifang, Yang Hongyi, Lin Chun, Chen Xiong
Department of Oncology, Fuzhou General Teaching Hospital of Fujian University of Traditional Chinese Medicine, 350001, Fuzhou, Fujian, PR China.
Department of Oncology, Mengchao Hepatobiliary Hospital of Fujian Medical University, 350028, Fuzhou, Fujian, PR China.
J Cancer. 2025 Jun 12;16(8):2663-2672. doi: 10.7150/jca.111026. eCollection 2025.
Targeted combined immunotherapy (TCI) has shown certain antitumor effects in patients with unresectable hepatocellular carcinoma(uHCC), but only a subset of patients benefit. This study aims to develop a Transformer-based radiomics model to predict the objective response to combined therapy in patients with uHCC. This multicenter, retrospective study involved 264 HCC patients who underwent contrast-enhanced MRI prior to immunotherapy. The patients were divided into a training cohort(n=180) and a validation cohort(n=84). Using a multi-instance learning approach, tumor lesions in multi-sequence MRI were segmented into cross-sectional images, and features were extracted using the ResNet50 model. The Transformer model was then trained to predict the objective response rate (ORR). The prediction process was visualized using Grad-CAM and SHAP algorithms. Model performance was assessed using ROC and DCA curves, while survival analysis was conducted using Kaplan-Meier curves. Among 264 patients, one achieved complete response (0.4%), 64 experienced partial response (24.2%). The ORR was 26.1% in the training group and 21.4% in the validation group. The model demonstrated high predictive accuracy, achieving a perfect area under the curve (AUC) of 1.000. Further validation using screenshot-based model inputs revealed an AUC of 0.929 (95% CI: 0.904, 0.947), confirming the model's clinical applicability. Kaplan-Meier analysis indicated that objective responders experienced better overall survival (OS) in both the training set (HR: 0.50, 95% CI: 0.27, 0.90) and the validation set (HR: 0.28, 95% CI: 0.08, 0.91). The deep learning framework combining ResNet50 and Transformer has proven its clinical applicability in predicting and assessing the efficacy of targeted combination immunotherapy in unresectable hepatocellular carcinoma, providing crucial guidance for clinical treatment decisions.
靶向联合免疫疗法（TCI）在不可切除肝细胞癌（uHCC）患者中已显示出一定的抗肿瘤效果，但只有一部分患者受益。本研究旨在开发一种基于Transformer的放射组学模型，以预测uHCC患者对联合治疗的客观反应。这项多中心回顾性研究纳入了264例在免疫治疗前接受过对比增强MRI检查的HCC患者。患者被分为训练队列（n = 180）和验证队列（n = 84）。采用多实例学习方法，将多序列MRI中的肿瘤病变分割为横断面图像，并使用ResNet50模型提取特征。然后训练Transformer模型来预测客观缓解率（ORR）。使用Grad-CAM和SHAP算法对预测过程进行可视化。使用ROC和DCA曲线评估模型性能，同时使用Kaplan-Meier曲线进行生存分析。在264例患者中，1例达到完全缓解（0.4%），64例出现部分缓解（24.2%）。训练组的ORR为26.1%，验证组为21.4%。该模型显示出较高的预测准确性，曲线下面积（AUC）达到完美的1.000。使用基于截图的模型输入进行进一步验证，AUC为0.929（95%CI：0.904，0.947），证实了该模型的临床适用性。Kaplan-Meier分析表明，在训练集（HR：0.50，95%CI：0.27，0.90）和验证集（HR：0.28，95%CI：0.08，0.91）中，客观缓解者的总生存期（OS）均更好。结合ResNet50和Transformer的深度学习框架已证明其在预测和评估不可切除肝细胞癌靶向联合免疫疗法疗效方面的临床适用性，为临床治疗决策提供了关键指导。
