Communications medicine · 2026
A deep learning model for the diagnosis of gastric neuroendocrine carcinoma.
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Zhu Tianchen, Zhao Zihan, Wang Chao, Zhang Xinke, Zheng Lin, Chen Wenxu, Zhou Zhengyi, Liao Zhiwei, Huang Yan, Cai Muyan, Lai Junpeng
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.