Abstract
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.
Plain language summary
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.
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Code availability
Source code is at https://github.com/Kepler1647b/G-NECNet (https://doi.org/10.5281/zenodo.18059065)33.
Data availability
All relevant data are available upon request, but cannot be shared publicly. Restrictions are applied to the whole slide images and annotation data of Internal cohort, External-cohort, and Consultation-cohort, which are used with institutional permission via IRB approval for the current study, and thus are not publicly available due to patient privacy obligations. All data supporting the findings of this study are available on request for non-commercial and academic purposes from the corresponding author, M.C. (caimy@sysucc.org.cn) within 10 working days. We do not require you to sign a data use agreement. Processed data can be reproduced stably by the source code. Supplementary Data are provided with this paper (Supplementary Data 1). The source data for Fig. 2 and Table 1 are in Supplementary Data 1.
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Acknowledgements
This work was supported by grants from the National Key R&D Program of China (No. 2021YFA1300201), the National Natural Science Foundation of China (Grant Nos. 81972227, 82172646, 81872001, and 82073189), and the Guangdong Esophageal Cancer Institute Science and Technology Program (No. M202108).
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Z.L., Y.H., M.C., and J.L. conceived and designed the study. T.Z., C.W., and X.Z. collected the samples and acquired the image data. Z. Zhao performed the machine learning. M.C., L.Z., and W.C. conducted the reader study. T.C. and Z. Zhou did the statistical analyses. All authors vouch for the data, analyses, and interpretations. T.Z. and M.C. wrote the first draft of the manuscript, and all authors reviewed, contributed to, and approved the manuscript.
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Communications Medicine thanks Wataru Uegami and Bing Ren for their contribution to the peer review of this work. A peer review file is available.
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Zhu, T., Zhao, Z., Wang, C. et al. A deep learning model for the diagnosis of gastric neuroendocrine carcinoma. Commun Med (2026). https://doi.org/10.1038/s43856-026-01382-3
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DOI: https://doi.org/10.1038/s43856-026-01382-3
