Department of Gastrointestinal Surgery, First Affiliated Hospital of Sun Yat-sen University, Zhongshan 2nd Street, No. 58, Guangzhou, 510080, 86, Guangdong, China.
Guangdong Provincial Key Laboratory of Digestive Cancer Research, Digestive Diseases Center, Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen, Guangdong, China.
J Transl Med. 2024 May 8;22(1):438. doi: 10.1186/s12967-024-05262-z.
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.
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).
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).
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.
先前，晚期不可切除胃癌（GC）患者仅接受化疗作为一线治疗。然而，随着食品和药物管理局（FDA）于 2022 年批准程序性死亡蛋白 1（PD-1）抑制剂联合化疗作为晚期不可切除 GC 的一线治疗方法，患者从中显著获益。然而，高昂的费用和潜在的不良反应需要进行精确的患者选择。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方面。我们的研究利用 DL 分析病理图像，旨在预测晚期 GC 一线 PD-1 联合化疗的反应。
在这项多中心回顾性分析中，我们从四个医疗中心收集了晚期 GC 患者的苏木精和伊红（H&E）染色切片。根据 iRECIST 1.1 标准，在全面接受一线 PD-1 免疫治疗联合化疗后，评估治疗反应。我们采用三种 DL 模型进行集成方法，创建一种新的组织病理学生物标志物——免疫检查点抑制剂反应评分（ICIsRS），它源自全切片图像（WSI）。
通过分析 264 名晚期 GC 患者的 313 张 WSI 的 148181 个斑块，集成模型表现出卓越的预测准确性，从而创建了 ICIsNet。该模型在四个测试数据集中均表现出稳健的性能，AUC 值分别为 0.92、0.95、0.96 和 1。基于 ICIsRS 构建的箱线图显示，良好反应和不良反应之间存在显著差异（所有 p 值均 < = 0.001）。
ICIsRS 是一种源自 WSI 的 DL 衍生生物标志物，可有效预测晚期 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方法，并允许根据患者独特的反应情况制定更个体化和潜在有效的治疗策略。
