利用人类专家和深度学习系统对前列腺癌自发荧光虚拟染色系统进行临床级验证。

IF 7.1 1区 医学 Q1 PATHOLOGY Modern Pathology Pub Date : 2024-07-26 DOI:10.1016/j.modpat.2024.100573
Pok Fai Wong , Carson McNeil , Yang Wang , Jack Paparian , Charles Santori , Michael Gutierrez , Andrew Homyk , Kunal Nagpal , Tiam Jaroensri , Ellery Wulczyn , Tadayuki Yoshitake , Julia Sigman , David F. Steiner , Sudha Rao , Po-Hsuan Cameron Chen , Luke Restorick , Jonathan Roy , Peter Cimermancic
{"title":"利用人类专家和深度学习系统对前列腺癌自发荧光虚拟染色系统进行临床级验证。","authors":"Pok Fai Wong ,&nbsp;Carson McNeil ,&nbsp;Yang Wang ,&nbsp;Jack Paparian ,&nbsp;Charles Santori ,&nbsp;Michael Gutierrez ,&nbsp;Andrew Homyk ,&nbsp;Kunal Nagpal ,&nbsp;Tiam Jaroensri ,&nbsp;Ellery Wulczyn ,&nbsp;Tadayuki Yoshitake ,&nbsp;Julia Sigman ,&nbsp;David F. Steiner ,&nbsp;Sudha Rao ,&nbsp;Po-Hsuan Cameron Chen ,&nbsp;Luke Restorick ,&nbsp;Jonathan Roy ,&nbsp;Peter Cimermancic","doi":"10.1016/j.modpat.2024.100573","DOIUrl":null,"url":null,"abstract":"<div><p>The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously validated Gleason scoring model, and an expert panel, on a large data set of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology.</p></div>","PeriodicalId":18706,"journal":{"name":"Modern Pathology","volume":"37 11","pages":"Article 100573"},"PeriodicalIF":7.1000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer\",\"authors\":\"Pok Fai Wong ,&nbsp;Carson McNeil ,&nbsp;Yang Wang ,&nbsp;Jack Paparian ,&nbsp;Charles Santori ,&nbsp;Michael Gutierrez ,&nbsp;Andrew Homyk ,&nbsp;Kunal Nagpal ,&nbsp;Tiam Jaroensri ,&nbsp;Ellery Wulczyn ,&nbsp;Tadayuki Yoshitake ,&nbsp;Julia Sigman ,&nbsp;David F. Steiner ,&nbsp;Sudha Rao ,&nbsp;Po-Hsuan Cameron Chen ,&nbsp;Luke Restorick ,&nbsp;Jonathan Roy ,&nbsp;Peter Cimermancic\",\"doi\":\"10.1016/j.modpat.2024.100573\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously validated Gleason scoring model, and an expert panel, on a large data set of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology.</p></div>\",\"PeriodicalId\":18706,\"journal\":{\"name\":\"Modern Pathology\",\"volume\":\"37 11\",\"pages\":\"Article 100573\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Modern Pathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0893395224001534\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Modern Pathology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0893395224001534","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PATHOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

前列腺腺癌和导管内癌(IDC-P)的组织诊断包括苏木精和伊红(H&E)染色的肿瘤形态格里森分级,以及 PIN-4 染色(CK5/6、P63、AMACR)的免疫组化(IHC)标记。在这项工作中,我们利用高通量高光谱荧光显微镜和人工智能与机器学习创建了一个自动化系统,可从未被染料的前列腺组织中生成虚拟 H&E 和 PIN-4 IHC 染色。我们证明,虚拟染色机模型可以生成适合泌尿生殖系统病理学家诊断的高质量图像。具体来说,我们通过广泛的人工审查和计算分析,利用先前验证过的格里森评分模型和专家小组,在大量测试切片数据集上验证了我们的系统。这项研究扩展了我们之前在自发荧光虚拟染色方面的工作,证明了这项技术在前列腺癌方面的临床实用性,并为数字病理学的定性和定量评估提供了严格的标准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Clinical-Grade Validation of an Autofluorescence Virtual Staining System With Human Experts and a Deep Learning System for Prostate Cancer

The tissue diagnosis of adenocarcinoma and intraductal carcinoma of the prostate includes Gleason grading of tumor morphology on the hematoxylin and eosin stain and immunohistochemistry markers on the prostatic intraepithelial neoplasia-4 stain (CK5/6, P63, and AMACR). In this work, we create an automated system for producing both virtual hematoxylin and eosin and prostatic intraepithelial neoplasia-4 immunohistochemistry stains from unstained prostate tissue using a high-throughput hyperspectral fluorescence microscope and artificial intelligence and machine learning. We demonstrate that the virtual stainer models can produce high-quality images suitable for diagnosis by genitourinary pathologists. Specifically, we validate our system through extensive human review and computational analysis, using a previously validated Gleason scoring model, and an expert panel, on a large data set of test slides. This study extends our previous work on virtual staining from autofluorescence, demonstrates the clinical utility of this technology for prostate cancer, and exemplifies a rigorous standard of qualitative and quantitative evaluation for digital pathology.

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
Modern Pathology
Modern Pathology 医学-病理学
CiteScore
14.30
自引率
2.70%
发文量
174
审稿时长
18 days
期刊介绍: Modern Pathology, an international journal under the ownership of The United States & Canadian Academy of Pathology (USCAP), serves as an authoritative platform for publishing top-tier clinical and translational research studies in pathology. Original manuscripts are the primary focus of Modern Pathology, complemented by impactful editorials, reviews, and practice guidelines covering all facets of precision diagnostics in human pathology. The journal's scope includes advancements in molecular diagnostics and genomic classifications of diseases, breakthroughs in immune-oncology, computational science, applied bioinformatics, and digital pathology.
期刊最新文献
"Introducing an Essential 7-Part AI Review Series: A Guided Journey into the Future of Pathology & Medicine". Statistics of Generative AI & Non-Generative Predictive Analytics Machine Learning in Medicine. Congenital peribronchial myofibroblastic tumors harbor a recurrent EGFR kinase domain duplication. Extra-Axial Poorly Differentiated Chordoma: Clinicopathologic and Molecular Genetic Characterization. Fast Processing of Electron Microscopic Specimen Preserved Ultrastructure of Glomeruli and Electron Dense Deposits in Diagnostic Renal Biopsies: A Prospective and Retrospective Comparative Study.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1