利用高次谐波显微镜和人工智能分析对肺和胸膜活检组织进行快速现场组织学分析。

IF 7.1 1区 医学 Q1 PATHOLOGY Modern Pathology Pub Date : 2024-10-16 DOI:10.1016/j.modpat.2024.100633
Laura M G van Huizen, Max Blokker, Johannes M A Daniels, Teodora Radonic, Jan H von der Thüsen, Mitko Veta, Jouke T Annema, Marie Louise Groot
{"title":"利用高次谐波显微镜和人工智能分析对肺和胸膜活检组织进行快速现场组织学分析。","authors":"Laura M G van Huizen, Max Blokker, Johannes M A Daniels, Teodora Radonic, Jan H von der Thüsen, Mitko Veta, Jouke T Annema, Marie Louise Groot","doi":"10.1016/j.modpat.2024.100633","DOIUrl":null,"url":null,"abstract":"<p><p>Lung cancer is both one of the most prevalent and lethal cancers. To improve health outcomes while reducing the healthcare burden, it becomes crucial to move towards early detection and cost-effective workflows. Currently there is no method for on-site rapid histological feedback on biopsies taken in diagnostic endoscopic or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. Here, we report the feasibility of a HHG portable microscope in the clinical workflow in terms of acquisition time, image quality and diagnostic accuracy in suspected pulmonary and pleural malignancy. 109 biopsies of 47 patients were imaged and a biopsy overview image was provided within a median of 6 minutes after excision. The assessment by pathologists and an artificial intelligence (AI) algorithm showed that image quality was sufficient for a malignancy or non-malignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide rapid pathology outcome on biopsy samples enabling immediate diagnosis and (local) treatment.</p>","PeriodicalId":18706,"journal":{"name":"Modern Pathology","volume":null,"pages":null},"PeriodicalIF":7.1000,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Rapid on-site histology of lung and pleural biopsies using higher harmonic generation microscopy and artificial intelligence analysis.\",\"authors\":\"Laura M G van Huizen, Max Blokker, Johannes M A Daniels, Teodora Radonic, Jan H von der Thüsen, Mitko Veta, Jouke T Annema, Marie Louise Groot\",\"doi\":\"10.1016/j.modpat.2024.100633\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Lung cancer is both one of the most prevalent and lethal cancers. To improve health outcomes while reducing the healthcare burden, it becomes crucial to move towards early detection and cost-effective workflows. Currently there is no method for on-site rapid histological feedback on biopsies taken in diagnostic endoscopic or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. Here, we report the feasibility of a HHG portable microscope in the clinical workflow in terms of acquisition time, image quality and diagnostic accuracy in suspected pulmonary and pleural malignancy. 109 biopsies of 47 patients were imaged and a biopsy overview image was provided within a median of 6 minutes after excision. The assessment by pathologists and an artificial intelligence (AI) algorithm showed that image quality was sufficient for a malignancy or non-malignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide rapid pathology outcome on biopsy samples enabling immediate diagnosis and (local) treatment.</p>\",\"PeriodicalId\":18706,\"journal\":{\"name\":\"Modern Pathology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Modern Pathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.modpat.2024.100633\",\"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://doi.org/10.1016/j.modpat.2024.100633","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

肺癌是发病率和致死率最高的癌症之一。为了改善健康状况,同时减轻医疗负担,实现早期检测和具有成本效益的工作流程至关重要。目前,还没有一种方法能对内窥镜诊断或外科手术中的活组织切片进行现场快速组织学反馈。高次谐波发生(HHG)显微镜是一种基于激光的技术,可提供未经处理的组织图像。在此,我们报告了便携式高次谐波显微镜在临床工作流程中的可行性,包括采集时间、图像质量以及对疑似肺部和胸膜恶性肿瘤的诊断准确性。我们对 47 名患者的 109 例活检组织进行了成像,并在切除后 6 分钟内提供了活检组织概览图像。病理学家和人工智能(AI)算法的评估结果显示,97%的活检图像质量足以做出恶性或非恶性诊断,87%的 HHG 图像得到了病理学家的正确评分。因此,HHG 是为活检样本提供快速病理结果的绝佳候选方案,可实现即时诊断和(局部)治疗。
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
Rapid on-site histology of lung and pleural biopsies using higher harmonic generation microscopy and artificial intelligence analysis.

Lung cancer is both one of the most prevalent and lethal cancers. To improve health outcomes while reducing the healthcare burden, it becomes crucial to move towards early detection and cost-effective workflows. Currently there is no method for on-site rapid histological feedback on biopsies taken in diagnostic endoscopic or surgical procedures. Higher harmonic generation (HHG) microscopy is a laser-based technique that provides images of unprocessed tissue. Here, we report the feasibility of a HHG portable microscope in the clinical workflow in terms of acquisition time, image quality and diagnostic accuracy in suspected pulmonary and pleural malignancy. 109 biopsies of 47 patients were imaged and a biopsy overview image was provided within a median of 6 minutes after excision. The assessment by pathologists and an artificial intelligence (AI) algorithm showed that image quality was sufficient for a malignancy or non-malignancy diagnosis in 97% of the biopsies, and 87% of the HHG images were correctly scored by the pathologists. HHG is therefore an excellent candidate to provide rapid pathology outcome on biopsy samples enabling immediate diagnosis and (local) treatment.

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
Proximal and classic epithelioid sarcomas are distinct molecular entities defined by MYC/GATA3 and SOX17/endothelial markers, respectively. Refining Diagnostic Subtypes of Peripheral T-cell Lymphoma Using a Multiparameter Approach. Clinicopathologic characteristics and follow up outcomes of invasive breast carcinoma with different positive HER2 fluorescence in situ hybridization patterns: experience from a single academic institution. Evaluation of a task specific self-supervised learning framework in digital pathology relative to transfer learning approaches and existing foundation models. GLI1-Altered Mesenchymal Tumor-Multiomic Characterization of a Case Series and Patient-Level Meta-analysis of One Hundred Sixty-Seven Cases for Risk Stratification.
×
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