ArticleCell reports. Medicine2024
Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A universal visual foundation model for computational cytopathology.Nature cancer · 2026Article
- A multimodal approach integrating NK cell-associated gene signatures and pathomics to predict colon adenocarcinoma prognosis.Scientific reports · 2026Article
- A deep learning-based automated pipeline for colorectal cancer detection in contrast-enhanced CT images.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2026Article
- A deep learning model for the diagnosis of gastric neuroendocrine carcinoma.Communications medicine · 2026Article
- A new, machine learning-based approach to metastatic neuroendocrine tumors of unknown origin.Journal of neuroendocrinology · 2026Article
- Prior knowledge-guided multimodal deep learning system for biomarker exploration and prognosis prediction of urothelial carcinoma.NPJ digital medicine · 2025Article
- Clinicopathological characteristics and treatment of patients with colorectal neuroendocrine carcinoma: A retrospective study of 157 cases.Oncology letters · 2025Article
- Exploring the role of neutrophil extracellular traps in colorectal cancer: Insights from single-cell sequencing.World journal of gastrointestinal oncology · 2025Review
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Authors and funding
15 authors.
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Abstract
Colorectal neuroendocrine tumors (NETs) differ significantly from colorectal carcinoma (CRC) in terms of treatment strategy and prognosis, necessitating a cost-effective approach for accurate discrimination. Here, we propose an approach for distinguishing between colorectal NET and CRC based on pathological images by utilizing pathological prior information to facilitate the generation of robust slide-level features. By calculating the similarity between morphological descriptions and patches, our approach selects only 2% of the diagnostically relevant patches for both training and inference, achieving an area under the receiver operating characteristic curve (AUROC) of 0.9974 on the internal dataset, and AUROCs of 0.9724 and 0.9513 on two external datasets. Our model effectively identifies NETs from CRCs, reducing unnecessary immunohistochemical tests and enhancing the precise treatment for patients with colorectal tumors. Our approach also enables researchers to investigate methods with high accuracy and low computational complexity, thereby advancing the application of artificial intelligence in clinical settings.
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