Yi Pan, Hongtian Dai, Shuhao Wang, Lang Wang, Qiting Li, Wenmiao Wang, Jiangtao Li, Dan Qi, Zhaoyang Yang, Jia Jia, Yaxi Wang, Qing Fang, Lin Li, Weixun Zhou, Zhigang Song, Shuangmei Zou
{"title":"通过基于人工智能的病理学对淋巴结进行临床适用的泛源癌症检测。","authors":"Yi Pan, Hongtian Dai, Shuhao Wang, Lang Wang, Qiting Li, Wenmiao Wang, Jiangtao Li, Dan Qi, Zhaoyang Yang, Jia Jia, Yaxi Wang, Qing Fang, Lin Li, Weixun Zhou, Zhigang Song, Shuangmei Zou","doi":"10.1159/000539010","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent.</p><p><strong>Methods: </strong>Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers.</p><p><strong>Results: </strong>It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925.</p><p><strong>Conclusion: </strong>Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.</p>","PeriodicalId":19805,"journal":{"name":"Pathobiology","volume":null,"pages":null},"PeriodicalIF":3.5000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology.\",\"authors\":\"Yi Pan, Hongtian Dai, Shuhao Wang, Lang Wang, Qiting Li, Wenmiao Wang, Jiangtao Li, Dan Qi, Zhaoyang Yang, Jia Jia, Yaxi Wang, Qing Fang, Lin Li, Weixun Zhou, Zhigang Song, Shuangmei Zou\",\"doi\":\"10.1159/000539010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent.</p><p><strong>Methods: </strong>Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers.</p><p><strong>Results: </strong>It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925.</p><p><strong>Conclusion: </strong>Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.</p>\",\"PeriodicalId\":19805,\"journal\":{\"name\":\"Pathobiology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.5000,\"publicationDate\":\"2024-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Pathobiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1159/000539010\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/5/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q3\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Pathobiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1159/000539010","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/5/8 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
Clinically Applicable Pan-Origin Cancer Detection for Lymph Nodes via Artificial Intelligence-Based Pathology.
Introduction: Lymph node metastasis is one of the most common ways of tumour metastasis. The presence or absence of lymph node involvement influences the cancer's stage, therapy, and prognosis. The integration of artificial intelligence systems in the histopathological diagnosis of lymph nodes after surgery is urgent.
Methods: Here, we propose a pan-origin lymph node cancer metastasis detection system. The system is trained by over 700 whole-slide images (WSIs) and is composed of two deep learning models to locate the lymph nodes and detect cancers.
Results: It achieved an area under the receiver operating characteristic curve (AUC) of 0.958, with a 95.2% sensitivity and 72.2% specificity, on 1,402 WSIs from 49 organs at the National Cancer Center, China. Moreover, we demonstrated that the system could perform robustly with 1,051 WSIs from 52 organs from another medical centre, with an AUC of 0.925.
Conclusion: Our research represents a step forward in a pan-origin lymph node metastasis detection system, providing accurate pathological guidance by reducing the probability of missed diagnosis in routine clinical practice.
期刊介绍:
''Pathobiology'' offers a valuable platform for the publication of high-quality original research into the mechanisms underlying human disease. Aiming to serve as a bridge between basic biomedical research and clinical medicine, the journal welcomes articles from scientific areas such as pathology, oncology, anatomy, virology, internal medicine, surgery, cell and molecular biology, and immunology. Published bimonthly, ''Pathobiology'' features original research papers and reviews on translational research. The journal offers the possibility to publish proceedings of meetings dedicated to one particular topic.