Work

A deep learning model for the diagnosis of gastric neuroendocrine carcinoma

Year: 2026

Type: article

Abstract: Gastric neuroendocrine carcinoma (G-NEC) presents with clinical and pathological features that closely resemble those of gastric adenocarcinoma (GC), often complicating differential diagnosis. However, G-NEC is markedly more aggressive and associated with a significantly poorer prognosis, necessitating accurate and timely identification to guide appropriate therapeutic interventions. In response to this clinical need, we developed G-NECNet, a deep convolutional neural network tailored to detect G-NEC from histopathological whole-slide images. The model demonstrates excellent diagnostic performance, yielding an average area under the receiver operating curve (AUROC) of 0.993 in the internal validation cohort, 0.985 on an external single-institutional dataset, and 1.000 on an external multi-institutional consultation dataset. These consistently high AUROC values highlight the robustness, accuracy, and generalizability of G-NECNet across diverse clinical settings. The integration of G-NECNet into routine diagnostic workflows may not only improve the precision of G-NEC classification but also reduce misdiagnosis-related healthcare costs, offering a practical and scalable solution for clinical application. Zhu, Zhao, Wang et al. describe G-NECNet, a deep learning model for detecting gastric neuroendocrine carcinoma (G-NEC) from H&E-stained biopsy whole-slide images without the need for additional immunohistochemistry. G-NECNet achieves high diagnostic accuracy across multiple datasets. This study aims to improve the diagnosis of gastric neuroendocrine carcinoma (G-NEC), a rare but aggressive stomach cancer often mistaken for the more common stomach cancer called gastric adenocarcinoma. Since accurate diagnosis typically necessitates time-consuming and resource-intensive staining of sections from the tumor, we developed G-NECNet, a computational model that analyzes routine tumor images to distinguish G-NEC precisely. The model works well across multiple datasets, demonstrating high reliability and generalizability in different clinical settings. These findings suggest that G-NECNet could assist pathologists in making faster and more accurate diagnoses of G-NEC. Integrating this tool into routine diagnostic workflows may help reduce errors, improve patient outcomes, and lower healthcare costs, providing a practical and scalable solution for clinical application.

Source: Communications Medicine

DOI: 10.1038/s43856-026-01382-3