非小细胞肺癌中标准化程序性死亡配体1表达评估的人工智能驱动图像分析。

IF 2.3 3区 医学 Q2 PATHOLOGY
Chong Ge, Yi Shi, Wei Wang, Anli Zhang, Mengqi Huang, Fang Zhao, Ao Li, Zhenzhong Feng, Minghui Wang, Haibo Wu
{"title":"非小细胞肺癌中标准化程序性死亡配体1表达评估的人工智能驱动图像分析。","authors":"Chong Ge, Yi Shi, Wei Wang, Anli Zhang, Mengqi Huang, Fang Zhao, Ao Li, Zhenzhong Feng, Minghui Wang, Haibo Wu","doi":"10.1186/s13000-025-01707-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Accurate assessment of programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) expression is critical for immunotherapy in patients with non-small cell lung cancer (NSCLC). Yet, interpreting its staining is challenging, time-consuming, and causes inter-observer variability, potentially mis-stratifying patients. This necessitates the development of an artificial intelligence (AI) model to effectively quantify PD-L1 expression. Hence, we developed an AI-based deep-learning approach to automatically assess PD-L1 expression in NSCLC using IHC 22C3 assay-stained whole slide images (WSIs).</p><p><strong>Methods: </strong>A total of 706 patients with NSCLC were included in this study and 1212 WSIs were collected from three distinct study cohorts. We accurately matched the hematoxylin and eosin-stained images of the internal dataset with the IHC WSIs. Foreground regions containing tumor tissue were extracted from WSIs, and a multi-granular multiple-instance learning approach employing instance embeddings with coarse and fine granularities was implemented to extract patch-level morphological features. A multi-grained expression interpreter-based model aggregated these features to stratify PD-L1 expression status.</p><p><strong>Results: </strong>The model showed strong interpretive ability in all three cohorts and wide applicability to different specimen types. The macro-average area under the receiver operating characteristic curve (AUC) were 0.940/0.915/0.944 for surgical specimens, 0.955/0.844/0.865 for biopsy specimens, and 0.901/0.958/0.883 for metastases.</p><p><strong>Conclusion: </strong>This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.</p>","PeriodicalId":11237,"journal":{"name":"Diagnostic Pathology","volume":"20 1","pages":"106"},"PeriodicalIF":2.3000,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465877/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer.\",\"authors\":\"Chong Ge, Yi Shi, Wei Wang, Anli Zhang, Mengqi Huang, Fang Zhao, Ao Li, Zhenzhong Feng, Minghui Wang, Haibo Wu\",\"doi\":\"10.1186/s13000-025-01707-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Accurate assessment of programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) expression is critical for immunotherapy in patients with non-small cell lung cancer (NSCLC). Yet, interpreting its staining is challenging, time-consuming, and causes inter-observer variability, potentially mis-stratifying patients. This necessitates the development of an artificial intelligence (AI) model to effectively quantify PD-L1 expression. Hence, we developed an AI-based deep-learning approach to automatically assess PD-L1 expression in NSCLC using IHC 22C3 assay-stained whole slide images (WSIs).</p><p><strong>Methods: </strong>A total of 706 patients with NSCLC were included in this study and 1212 WSIs were collected from three distinct study cohorts. We accurately matched the hematoxylin and eosin-stained images of the internal dataset with the IHC WSIs. Foreground regions containing tumor tissue were extracted from WSIs, and a multi-granular multiple-instance learning approach employing instance embeddings with coarse and fine granularities was implemented to extract patch-level morphological features. A multi-grained expression interpreter-based model aggregated these features to stratify PD-L1 expression status.</p><p><strong>Results: </strong>The model showed strong interpretive ability in all three cohorts and wide applicability to different specimen types. The macro-average area under the receiver operating characteristic curve (AUC) were 0.940/0.915/0.944 for surgical specimens, 0.955/0.844/0.865 for biopsy specimens, and 0.901/0.958/0.883 for metastases.</p><p><strong>Conclusion: </strong>This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.</p>\",\"PeriodicalId\":11237,\"journal\":{\"name\":\"Diagnostic Pathology\",\"volume\":\"20 1\",\"pages\":\"106\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2025-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12465877/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diagnostic Pathology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13000-025-01707-1\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"PATHOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diagnostic Pathology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13000-025-01707-1","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PATHOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

背景:准确评估程序性死亡配体1 (PD-L1)免疫组织化学(IHC)表达对于非小细胞肺癌(NSCLC)患者的免疫治疗至关重要。然而,解释其染色是具有挑战性的,耗时的,并导致观察者之间的差异,潜在的错误分层患者。这就需要开发一种人工智能(AI)模型来有效量化PD-L1的表达。因此,我们开发了一种基于人工智能的深度学习方法,使用IHC 22C3染色全切片图像(WSIs)自动评估非小细胞肺癌中PD-L1的表达。方法:本研究共纳入706例NSCLC患者,并从三个不同的研究队列中收集1212例wsi。我们将内部数据集的苏木精和伊红染色图像与IHC WSIs精确匹配。从wsi中提取含有肿瘤组织的前景区域,并采用基于粗粒度和细粒度实例嵌入的多粒度多实例学习方法提取斑块级形态特征。一个基于多粒度表达解释器的模型将这些特征聚合在一起,对PD-L1的表达状态进行分层。结果:该模型在三个队列中具有较强的解释能力,对不同的标本类型具有广泛的适用性。手术标本受者工作特征曲线下宏观平均面积(AUC)为0.940/0.915/0.944,活检标本为0.955/0.844/0.865,转移标本为0.901/0.958/0.883。结论:本研究强调了深度学习在从复杂IHC图像中自动、快速、准确地推断PD-L1表达方面的潜在优势。它还展示了人工智能框架如何改善当前PD-L1检测方法中的常规数字病理工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence-driven image analysis for standardised programmed death-ligand 1 expression evaluation in non-small cell lung cancer.

Background: Accurate assessment of programmed death-ligand 1 (PD-L1) immunohistochemical (IHC) expression is critical for immunotherapy in patients with non-small cell lung cancer (NSCLC). Yet, interpreting its staining is challenging, time-consuming, and causes inter-observer variability, potentially mis-stratifying patients. This necessitates the development of an artificial intelligence (AI) model to effectively quantify PD-L1 expression. Hence, we developed an AI-based deep-learning approach to automatically assess PD-L1 expression in NSCLC using IHC 22C3 assay-stained whole slide images (WSIs).

Methods: A total of 706 patients with NSCLC were included in this study and 1212 WSIs were collected from three distinct study cohorts. We accurately matched the hematoxylin and eosin-stained images of the internal dataset with the IHC WSIs. Foreground regions containing tumor tissue were extracted from WSIs, and a multi-granular multiple-instance learning approach employing instance embeddings with coarse and fine granularities was implemented to extract patch-level morphological features. A multi-grained expression interpreter-based model aggregated these features to stratify PD-L1 expression status.

Results: The model showed strong interpretive ability in all three cohorts and wide applicability to different specimen types. The macro-average area under the receiver operating characteristic curve (AUC) were 0.940/0.915/0.944 for surgical specimens, 0.955/0.844/0.865 for biopsy specimens, and 0.901/0.958/0.883 for metastases.

Conclusion: This study emphasizes the potential benefits of deep learning in automatically, rapidly, and accurately inferring PD-L1 expression from complex IHC images. It also showcases how AI frameworks can improve routine digital pathology workflows in current PD-L1 detection methods.

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
Diagnostic Pathology
Diagnostic Pathology 医学-病理学
CiteScore
4.60
自引率
0.00%
发文量
93
审稿时长
1 months
期刊介绍: Diagnostic Pathology is an open access, peer-reviewed, online journal that considers research in surgical and clinical pathology, immunology, and biology, with a special focus on cutting-edge approaches in diagnostic pathology and tissue-based therapy. The journal covers all aspects of surgical pathology, including classic diagnostic pathology, prognosis-related diagnosis (tumor stages, prognosis markers, such as MIB-percentage, hormone receptors, etc.), and therapy-related findings. The journal also focuses on the technological aspects of pathology, including molecular biology techniques, morphometry aspects (stereology, DNA analysis, syntactic structure analysis), communication aspects (telecommunication, virtual microscopy, virtual pathology institutions, etc.), and electronic education and quality assurance (for example interactive publication, on-line references with automated updating, etc.).
×
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:604180095
Book学术官方微信