Wu Ji, Zheng Zhigang, Li Jian, Shen Xiping, Huang Bo
Department of General Surgery, Suzhou Ninth Hospital Affiliated to Soochow University, Suzhou, China.
Department of Interventional Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Eur Radiol. 2025 May 8. doi: 10.1007/s00330-025-11645-7.
Accurate estimation of treatment response can help clinicians identify patients who would potentially benefit from systemic therapy. This study aimed to develop and externally validate a model for predicting treatment response to systemic therapy in advanced gallbladder cancer (GBC).
We recruited 399 eligible GBC patients across four institutions. Multivariable logistic regression analysis was performed to identify independent clinical factors related to therapeutic efficacy. This deep learning (DL) radiomics signature was developed for predicting treatment response using multiphase enhanced CT images. Then, the DL radiomic-clinical (DLRSC) model was built by combining the DL signature and significant clinical factors, and its predictive performance was evaluated using area under the curve (AUC). Gradient-weighted class activation mapping analysis was performed to help clinicians better understand the predictive results. Furthermore, patients were stratified into low- and high-score groups by the DLRSC model. The progression-free survival (PFS) and overall survival (OS) between the two different groups were compared.
Multivariable analysis revealed that tumor size was a significant predictor of efficacy. The DLRSC model showed great predictive performance, with AUCs of 0.86 (95% CI, 0.82-0.89) and 0.84 (95% CI, 0.80-0.87) in the internal and external test datasets, respectively. This model showed great discrimination, calibration, and clinical utility. Moreover, Kaplan-Meier survival analysis revealed that low-score group patients who were insensitive to systemic therapy predicted by the DLRSC model had worse PFS and OS.
The DLRSC model allows for predicting treatment response in advanced GBC patients receiving systemic therapy. The survival benefit provided by the DLRSC model was also assessed.
Question No effective tools exist for identifying patients who would potentially benefit from systemic therapy in clinical practice. Findings Our combined model allows for predicting treatment response to systemic therapy in advanced gallbladder cancer. Clinical relevance With the help of this model, clinicians could inform patients of the risk of potential ineffective treatment. Such a strategy can reduce unnecessary adverse events and effectively help reallocate societal healthcare resources.
准确评估治疗反应有助于临床医生识别可能从全身治疗中获益的患者。本研究旨在开发并外部验证一个预测晚期胆囊癌（GBC）全身治疗反应的模型。
我们在四个机构招募了399例符合条件的GBC患者。进行多变量逻辑回归分析以确定与治疗效果相关的独立临床因素。使用多期增强CT图像开发了这种深度学习（DL）放射组学特征以预测治疗反应。然后，通过结合DL特征和显著临床因素构建了DL放射组学-临床（DLRSC）模型，并使用曲线下面积（AUC）评估其预测性能。进行梯度加权类激活映射分析以帮助临床医生更好地理解预测结果。此外，根据DLRSC模型将患者分为低分和高分两组。比较两组之间的无进展生存期（PFS）和总生存期（OS）。
多变量分析显示肿瘤大小是疗效的显著预测因素。DLRSC模型显示出良好的预测性能，内部和外部测试数据集的AUC分别为0.86（95%CI，0.82-0.89）和0.84（95%CI，0.80-0.87）。该模型显示出良好的区分度、校准度和临床实用性。此外，Kaplan-Meier生存分析显示，DLRSC模型预测对全身治疗不敏感的低分患者组的PFS和OS较差。
DLRSC模型能够预测接受全身治疗的晚期GBC患者的治疗反应。还评估了DLRSC模型提供的生存获益。
问题 在临床实践中，不存在有效的工具来识别可能从全身治疗中获益的患者。研究结果 我们的联合模型能够预测晚期胆囊癌全身治疗的反应。临床意义 借助该模型，临床医生可以告知患者潜在无效治疗的风险。这样的策略可以减少不必要的不良事件，并有效地帮助重新分配社会医疗资源。
