Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor

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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Publication Details

Journal
Cell Reports Medicine
Published
2024-10-01
DOI
https://doi.org/10.1016/j.xcrm.2024.101785
Citations
7
Primary Topic
Pancreatic and Hepatic Oncology Research
Type
article
Field-Weighted Citation Impact
1.98

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article

Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor

Muyan Cai, Bingzhong Jing, Xueyi Zheng, Jin‐Ling Duan et al.
7 citations
Cell Reports Medicine
Pancreatic and Hepatic Oncology Research
1.98
article

Deep learning model with pathological knowledge for detection of colorectal neuroendocrine tumor

Muyan Cai, Bingzhong Jing, Xueyi Zheng, Jin‐Ling Duan, Dan Xie, Shasha Liu, Ning Zhang, Haohua Chen, Yuqian Zhang, Ruixuan Wang, Haiyang He, Lin Yuan, Ke Zheng, Zihan Zhao, Yan Sun
article en
7 citations

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.

Cell Reports MedicineVol. 5(10)
Johns Hopkins University (US), Sun Yat-sen University (CN), Johns Hopkins Medicine (US), Tianjin Medical University Cancer Institute and Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Sun Yat-sen University Cancer Center (CN)
National Natural Science Foundation of China, Science and Technology Planning Project of Guangdong Province
Good health and well-being
Openalex Percentile: Top 11%
Pancreatic and Hepatic Oncology Research
1.98
DOIOpen Access PDFOpenAlex
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