biostudies-literatureUnknown8(1)Yan FShanghai Artificial Intelligence Laboratory, the Centre for Perceptual and Interactive Intelligence (CPII) Ltd, under the auspices of the Innovation and Technology Commission (ITC)’s InnoHK initiativeShanghai Artificial Intelligence Laboratory, the Shanghai Sailing ProgramDiffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overcoNPJ precision oncology76https://www.ebi.ac.uk/biostudies/studies/S-EPMC10973523biostudies-literatureArtificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.PMC10973523Zhou MDeng SWang CZhang SWang JDa QZhang WLiu YYi HFeng MZhu LZhang YZhang XWang XLin JYan FfalseArtificial intelligence-based assessment of PD-L1 expression in diffuse large B cell lymphoma.Diffuse large B cell lymphoma (DLBCL) is an aggressive blood cancer known for its rapid progression and high incidence. The growing use of immunohistochemistry (IHC) has significantly contributed to the detailed cell characterization, thereby playing a crucial role in guiding treatment strategies for DLBCL. In this study, we developed an AI-based image analysis approach for assessing PD-L1 expression in DLBCL patients. PD-L1 expression represents as a major biomarker for screening patients who can benefit from targeted immunotherapy interventions. In particular, we performed large-scale cell annotations in IHC slides, encompassing over 5101 tissue regions and 146,439 live cells. Extensive experiments in primary and validation cohorts demonstrated the defined quantitative rule helped overco2024-01-01T00:00:00Z2024 Mar2026-07-16T15:21:07.959Z2025-04-06T03:35:16.153ZS-EPMC109735233853873910.1038/s41698-024-00577-y