根据多模式数据，预测胃癌对抗HEH2治疗或抗HER2的合并免疫疗法
Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data
影响因子:52.70000
分区:医学1区 Top / 生化与分子生物学1区 细胞生物学1区
发表日期:2024 Aug 26
作者:
Zifan Chen, Yang Chen, Yu Sun, Lei Tang, Li Zhang, Yajie Hu, Meng He, Zhiwei Li, Siyuan Cheng, Jiajia Yuan, Zhenghang Wang, Yakun Wang, Jie Zhao, Jifang Gong, Liying Zhao, Baoshan Cao, Guoxin Li, Xiaotian Zhang, Bin Dong, Lin Shen
摘要
唯一使用单局模式数据通常无法捕获患者之间的复杂异质性，包括对抗HER2治疗的耐药性和联合治疗方案的结局的可变性，用于治疗HER2阳性胃癌（GC）。在许多研究中，这种方式赤字尚未完全考虑。此外，人工智能在预测治疗反应中的应用，特别是在诸如GC等复杂疾病中，仍处于起步阶段。因此，这项研究旨在使用一种全面的分析方法来准确预测HER2阳性GC患者的抗HEH2治疗或抗HER2联合免疫疗法的治疗反应。我们收集了来自429例患者的同类群体的放射学，病理学和临床信息的多模式数据：310例接受抗HER2治疗治疗，119例用抗HER2和抗PD-1/PD-1/PD-1/PD-L1抑制剂免疫疗法进行治疗。我们介绍了一种称为多模式模型（MUMO）的深度学习模型，该模型集成了这些数据以进行精确的治疗响应预测。 Mumo在抗HER2治疗的曲线评分下达到了一个面积，合并免疫疗法的面积为0.914。此外，被MUMO归类为低风险的患者表现出显着延长的无进展生存期和总生存期（对数秩检验，p <0.05）。这些发现不仅强调了多模式数据分析在增强HER2阳性胃癌的治疗评估和个性化医学方面的重要性，还强调了我们模型的潜在和临床价值。
Abstract
The sole use of single modality data often fails to capture the complex heterogeneity among patients, including the variability in resistance to anti-HER2 therapy and outcomes of combined treatment regimens, for the treatment of HER2-positive gastric cancer (GC). This modality deficit has not been fully considered in many studies. Furthermore, the application of artificial intelligence in predicting the treatment response, particularly in complex diseases such as GC, is still in its infancy. Therefore, this study aimed to use a comprehensive analytic approach to accurately predict treatment responses to anti-HER2 therapy or anti-HER2 combined immunotherapy in patients with HER2-positive GC. We collected multi-modal data, comprising radiology, pathology, and clinical information from a cohort of 429 patients: 310 treated with anti-HER2 therapy and 119 treated with a combination of anti-HER2 and anti-PD-1/PD-L1 inhibitors immunotherapy. We introduced a deep learning model, called the Multi-Modal model (MuMo), that integrates these data to make precise treatment response predictions. MuMo achieved an area under the curve score of 0.821 for anti-HER2 therapy and 0.914 for combined immunotherapy. Moreover, patients classified as low-risk by MuMo exhibited significantly prolonged progression-free survival and overall survival (log-rank test, P < 0.05). These findings not only highlight the significance of multi-modal data analysis in enhancing treatment evaluation and personalized medicine for HER2-positive gastric cancer, but also the potential and clinical value of our model.