PD-L1 Scoring Models for Non-Small Cell Lung Cancer in China: Current Status, AI-Assisted Solutions and Future Perspectives.

IF 2.3 3区 医学 Q3 ONCOLOGY
Ziling Huang, Shen Wang, Jiansong Zhou, Haiquan Chen, Yuan Li
{"title":"PD-L1 Scoring Models for Non-Small Cell Lung Cancer in China: Current Status, AI-Assisted Solutions and Future Perspectives.","authors":"Ziling Huang, Shen Wang, Jiansong Zhou, Haiquan Chen, Yuan Li","doi":"10.1111/1759-7714.70042","DOIUrl":null,"url":null,"abstract":"<p><p>Immunotherapy has revolutionized the diagnosis and treatment model for patients with advanced non-small cell lung cancer (NSCLC). Numerous clinical trials and real-world reports have confirmed that PD-L1 status is a key factor for the successful use of immunotherapy in NSCLC, by predicting clinical outcomes and identifying patients most likely to benefit from this treatment. Therefore, accurate and standardized evaluation of PD-L1 expression is crucial. Currently, PD-L1 testing in China faces several challenges, including a heavy pathologist workload, a shortage of highly trained pathologists plus the inadequate capacity of diagnostic laboratories, confusion around different scoring methods, cut-off values, and indications, and limited concordance between PD-L1 assays. In this review, we summarize the current status and limitations of PD-L1 testing for patients with NSCLC in China and discuss recent progress in artificial intelligence-assisted PD-L1 scoring. Our review aims to support improvements in clinical PD-L1 testing practice and optimization of the prognosis and outcomes of immunotherapy in this patient population.</p>","PeriodicalId":23338,"journal":{"name":"Thoracic Cancer","volume":"16 7","pages":"e70042"},"PeriodicalIF":2.3000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11973252/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Thoracic Cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1111/1759-7714.70042","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Immunotherapy has revolutionized the diagnosis and treatment model for patients with advanced non-small cell lung cancer (NSCLC). Numerous clinical trials and real-world reports have confirmed that PD-L1 status is a key factor for the successful use of immunotherapy in NSCLC, by predicting clinical outcomes and identifying patients most likely to benefit from this treatment. Therefore, accurate and standardized evaluation of PD-L1 expression is crucial. Currently, PD-L1 testing in China faces several challenges, including a heavy pathologist workload, a shortage of highly trained pathologists plus the inadequate capacity of diagnostic laboratories, confusion around different scoring methods, cut-off values, and indications, and limited concordance between PD-L1 assays. In this review, we summarize the current status and limitations of PD-L1 testing for patients with NSCLC in China and discuss recent progress in artificial intelligence-assisted PD-L1 scoring. Our review aims to support improvements in clinical PD-L1 testing practice and optimization of the prognosis and outcomes of immunotherapy in this patient population.

中国非小细胞肺癌的PD-L1评分模型:现状、人工智能辅助解决方案和未来展望
免疫疗法已经彻底改变了晚期非小细胞肺癌(NSCLC)患者的诊断和治疗模式。许多临床试验和现实世界的报告已经证实,通过预测临床结果和确定最有可能从这种治疗中受益的患者,PD-L1状态是成功使用免疫治疗非小细胞肺癌的关键因素。因此,准确、规范地评估PD-L1表达至关重要。目前,中国的PD-L1检测面临着几个挑战,包括病理学家工作量大、训练有素的病理学家短缺、诊断实验室能力不足、不同评分方法、临界值和适应症的混乱,以及PD-L1检测之间的一致性有限。在本文中,我们总结了中国NSCLC患者PD-L1检测的现状和局限性,并讨论了人工智能辅助PD-L1评分的最新进展。我们的综述旨在支持PD-L1临床检测实践的改进,并优化该患者群体的预后和免疫治疗结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Thoracic Cancer
Thoracic Cancer ONCOLOGY-RESPIRATORY SYSTEM
CiteScore
5.20
自引率
3.40%
发文量
439
审稿时长
2 months
期刊介绍: Thoracic Cancer aims to facilitate international collaboration and exchange of comprehensive and cutting-edge information on basic, translational, and applied clinical research in lung cancer, esophageal cancer, mediastinal cancer, breast cancer and other thoracic malignancies. Prevention, treatment and research relevant to Asia-Pacific is a focus area, but submissions from all regions are welcomed. The editors encourage contributions relevant to prevention, general thoracic surgery, medical oncology, radiology, radiation medicine, pathology, basic cancer research, as well as epidemiological and translational studies in thoracic cancer. Thoracic Cancer is the official publication of the Chinese Society of Lung Cancer, International Chinese Society of Thoracic Surgery and is endorsed by the Korean Association for the Study of Lung Cancer and the Hong Kong Cancer Therapy Society. The Journal publishes a range of article types including: Editorials, Invited Reviews, Mini Reviews, Original Articles, Clinical Guidelines, Technological Notes, Imaging in thoracic cancer, Meeting Reports, Case Reports, Letters to the Editor, Commentaries, and Brief Reports.
×
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