METHODS: In this multicenter retrospective analysis, Hematoxylin and Eosin (H&E)-stained slides were collected from advanced GC patients across four medical centers. Treatment response was evaluated according to iRECIST 1.1 criteria after a comprehensive first-line PD-1 immunotherapy combined with chemotherapy. Three DL models were employed in an ensemble approach to create the immune checkpoint inhibitors Response Score (ICIsRS) as a novel histopathological biomarker derived from Whole Slide Images (WSIs).
RESULTS: Analyzing 148,181 patches from 313 WSIs of 264 advanced GC patients, the ensemble model exhibited superior predictive accuracy, leading to the creation of ICIsNet. The model demonstrated robust performance across four testing datasets, achieving AUC values of 0.92, 0.95, 0.96, and 1 respectively. The boxplot, constructed from the ICIsRS, reveals statistically significant disparities between the well response and poor response (all p-values < = 0.001).
CONCLUSIONS: ICIsRS, a DL-derived biomarker from WSIs, effectively predicts advanced GC patients\' responses to PD-1 combined chemotherapy, offering a novel approach for personalized treatment planning and allowing for more individualized and potentially effective treatment strategies based on a patient\'s unique response situations.
方法:在这项多中心回顾性分析中,从四个医疗中心的晚期GC患者收集苏木精和伊红(H&E)染色的载玻片。在综合一线PD-1免疫疗法联合化疗后,根据iRECIST1.1标准评估治疗反应。在集成方法中采用三个DL模型来创建免疫检查点抑制剂反应评分(ICIsRS)作为源自全幻灯片图像(WSI)的新型组织病理学生物标志物。
结果:分析了264例晚期GC患者313个WSI的148,181个贴片,集成模型表现出优异的预测精度,导致ICIsNet的创建。该模型在四个测试数据集上表现出稳健的性能,AUC值分别为0.92、0.95、0.96和1。盒子情节,从ICIsRS建造,揭示了良好反应和不良反应之间的统计学显著差异(所有p值<=0.001)。
结论:ICIsRS,来自WSI的DL衍生生物标志物,有效预测晚期GC患者对PD-1联合化疗的反应,为个性化治疗计划提供了一种新的方法,并允许根据患者的独特反应情况制定更个性化和潜在有效的治疗策略。