PDF(Pubmed)
Abstract：
BACKGROUND: Advanced unresectable gastric cancer (GC) patients were previously treated with chemotherapy alone as the first-line therapy. However, with the Food and Drug Administration\'s (FDA) 2022 approval of programmed cell death protein 1 (PD-1) inhibitor combined with chemotherapy as the first-li ne treatment for advanced unresectable GC, patients have significantly benefited. However, the significant costs and potential adverse effects necessitate precise patient selection. In recent years, the advent of deep learning (DL) has revolutionized the medical field, particularly in predicting tumor treatment responses. Our study utilizes DL to analyze pathological images, aiming to predict first-line PD-1 combined chemotherapy response for advanced-stage GC.
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.
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.
摘要：
背景：晚期不可切除的胃癌（GC）患者以前曾单独使用化疗作为一线治疗。然而,随着食品和药物管理局（FDA）2022批准程序性细胞死亡蛋白1（PD-1）抑制剂联合化疗作为晚期不可切除的GC的第一个治疗方法，患者显著受益。然而,巨大的成本和潜在的不利影响需要精确的患者选择.近年来,深度学习（DL）的出现彻底改变了医学领域，特别是在预测肿瘤治疗反应。我们的研究利用DL分析病理图像，旨在预测一线PD-1联合化疗对晚期GC的反应。
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。
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。
