Huang Ziling, Wang Shen, Zhou Jiansong, Chen Haiquan, Li Yuan
Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China.
Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
Thorac Cancer. 2025 Apr;16(7):e70042. doi: 10.1111/1759-7714.70042.
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.
免疫疗法彻底改变了晚期非小细胞肺癌(NSCLC)患者的诊断和治疗模式。众多临床试验和真实世界报告证实,通过预测临床结果和识别最可能从该治疗中获益的患者,PD-L1状态是免疫疗法在NSCLC中成功应用的关键因素。因此,准确且标准化地评估PD-L1表达至关重要。目前,中国的PD-L1检测面临若干挑战,包括病理学家工作量繁重、训练有素的病理学家短缺以及诊断实验室能力不足、不同评分方法、临界值和适应症存在混淆,以及PD-L1检测之间的一致性有限。在本综述中,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临床PD-L1检测实践的改进以及该患者群体免疫治疗预后和结果的优化。