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。

IF 3.5 4区 医学 Q3 CELL BIOLOGY
Pathobiology Pub Date : 2024-01-01 Epub Date: 2024-05-08 DOI:10.1159/000539010
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}
引用次数: 0

摘要

淋巴结转移是肿瘤转移最常见的方式之一。淋巴结是否受累影响着癌症的分期、治疗和预后。将人工智能系统集成到术后淋巴结的组织病理学诊断中已迫在眉睫。在此,我们提出了一种泛原发癌淋巴结转移检测系统。该系统由两个深度学习模型组成,通过 700 多张全切片图像进行训练,以定位淋巴结和检测癌症。该系统在中国国家癌症中心 49 个器官的 1 402 张全切片图像(WSI)上的接收者操作特征曲线(ROC)下面积(AUC)为 0.958,灵敏度为 95.2%,特异度为 72.2%。此外,我们还证明了该系统在处理来自另一个医疗中心 52 个器官的 1,051 张 WSI 图像时表现良好,AUC 为 0.925。我们的研究标志着泛原发淋巴结转移检测系统向前迈进了一步,通过降低常规临床实践中的漏诊概率,提供准确的病理指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Pathobiology
Pathobiology 医学-病理学
CiteScore
8.50
自引率
0.00%
发文量
47
审稿时长
>12 weeks
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.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信