Gou Miaomiao, Zhang Hongtao, Qian Niansong, Zhang Yong, Sun Zeyu, Li Guang, Wang Zhikuan, Dai Guanghai
Department of Medical Oncology, The Fifth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, PR China.
Department of Thoracic Oncology, The Eighth Medical Center, Chinese People's Liberation Army General Hospital, Beijing, PR China.
Eur J Radiol Open. 2024 Dec 19;14:100626. doi: 10.1016/j.ejro.2024.100626. eCollection 2025 Jun.
Immunotherapy has become an option for the first-line therapy of advanced gastric cancer (GC), with improved survival. Our study aimed to investigate unresectable GC from an imaging perspective combined with clinicopathological variables to identify patients who were most likely to benefit from immunotherapy.
Patients with unresectable GC who were consecutively treated with immunotherapy at two different medical centers of Chinese PLA General Hospital were included and divided into the training and validation cohorts, respectively. A deep learning neural network, using a multimodal ensemble approach based on CT imaging data before immunotherapy, was trained in the training cohort to predict survival, and an internal validation cohort was constructed to select the optimal ensemble model. Data from another cohort were used for external validation. The area under the receiver operating characteristic curve was analyzed to evaluate performance in predicting survival. Detailed clinicopathological data and peripheral blood prior to immunotherapy were collected for each patient. Univariate and multivariable logistic regression analysis of imaging models and clinicopathological variables was also applied to identify the independent predictors of survival. A nomogram based on multivariable logistic regression was constructed.
A total of 79 GC patients in the training cohort and 97 patients in the external validation cohort were enrolled in this study. A multi-model ensemble approach was applied to train a model to predict the 1-year survival of GC patients. Compared to individual models, the ensemble model showed improvement in performance metrics in both the internal and external validation cohorts. There was a significant difference in overall survival (OS) among patients with different imaging models based on the optimum cutoff score of 0.5 (HR = 0.20, 95 % CI: 0.10-0.37, < 0.001). Multivariate Cox regression analysis revealed that the imaging models, PD-L1 expression, and lung immune prognostic index were independent prognostic factors for OS. We combined these variables and built a nomogram. The calibration curves showed that the C-index of the nomogram was 0.85 and 0.78 in the training and validation cohorts.
The deep learning model in combination with several clinical factors showed predictive value for survival in patients with unresectable GC receiving immunotherapy.
免疫疗法已成为晚期胃癌（GC）一线治疗的一种选择，可提高生存率。我们的研究旨在从影像学角度结合临床病理变量来研究不可切除的GC，以确定最有可能从免疫疗法中获益的患者。
纳入在中国人民解放军总医院两个不同医疗中心连续接受免疫疗法治疗的不可切除GC患者，并分别分为训练队列和验证队列。使用基于免疫疗法前CT成像数据的多模态整合方法的深度学习神经网络在训练队列中进行训练以预测生存率，并构建内部验证队列以选择最佳整合模型。来自另一个队列的数据用于外部验证。分析受试者工作特征曲线下面积以评估预测生存率的性能。收集每位患者免疫疗法前的详细临床病理数据和外周血。还对影像模型和临床病理变量进行单变量和多变量逻辑回归分析，以确定生存的独立预测因素。构建基于多变量逻辑回归的列线图。
本研究纳入了训练队列中的79例GC患者和外部验证队列中的97例患者。应用多模型整合方法训练一个模型来预测GC患者的1年生存率。与单个模型相比，整合模型在内部和外部验证队列中的性能指标均有所改善。基于最佳截断分数0.5，不同影像模型的患者总生存期（OS）存在显著差异（HR = 0.20，95%CI：0.10 - 0.37，<0.001）。多变量Cox回归分析显示，影像模型、PD-L1表达和肺免疫预后指数是OS的独立预后因素。我们将这些变量结合起来构建了一个列线图。校准曲线显示，列线图在训练队列和验证队列中的C指数分别为0.85和0.78。
深度学习模型结合多个临床因素对接受免疫疗法的不可切除GC患者的生存具有预测价值。
