Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter...

The existing TNM staging system provides insufficient prognostic information in gastric cancer (GC) patients. This study aims to establish a pathomics signature of GC (PS_GC) that uses deep learning (DL) to directly analyze H&E slides for predicting GC outcomes. We propose a multi-scale graph neural network with gated attention mechanism for multi-instance learning (MS-GMIL) for the construction of PS_GC. Moreover, transcriptomic data investigated the possible pathophysiological mechanisms of the PS_GC. The PS_GC was identified as an independent prognostic factor in all cohorts. Patients with stage II and III GC, along with a high PS_GC, showed considerable benefits from chemotherapy and an effective response to immunotherapy. The primary histological features underlying the PS_GC were tumor cell anaplasia, intraepithelial neoplasia, tumor-stroma fibrosis, and intestinal epithelial metaplasia. Moreover, the PS_GC was associated with cell cycle regulation, drug resistance pathways, and mechanisms of cancer progression. The PS_GC functions as a valuable tool in clinical decision-making for the management of GC, providing insights into the underlying pathogenic mechanisms.