1220P Comprehensive Diagnose of Programmed Death-Ligand 1 from Two-Dimensional to Three-Dimensional in Breast Cancer with Computer-Aided Artificial Intelligence System

Determination of programmed death-ligand (PD-L1) status in triple-negative breast cancer (BC) by immunohistochemical (IHC) assay represents has prognostic significance in association with immune checkpoint inhibitor (ICI) therapies. However, the evaluation of PD-L1 SP142 remains challenging for pathologists since the associated tumor microenvironment complicates the immune cell (IC) scoring. Moreover, PD-L1 distribution in tumor could also lead to a risk of inaccurate patient allocation for ICI. To accurately diagnose PD-L1 status as IC score, a computer-aided artificial intelligence (AI) system is required to precisely quantify PD-L1 expression in two-dimensional and three-dimensional (3D) pathological images. In this study, we proposed a computer-aided AI system for digital images of IHC and 3D immunofluorescence (IF) assays. This system consisted of machine learning algorithms for tumor/IC recognition, infiltration identification, and PD-L1 expression detection. Tissue clearing technology with IF and confocal microscopy were utilized to detect the spatial distribution of PD-L1 in BC specimens. The designed computer-aided AI system was based on a tumor segmentation model with a >80% accuracy and an IC recognition model with a 90% accuracy. For the IHC digital pathology, the computer-aided AI system achieved a concordance rate of 83% in comparison with PD-L1 IC interpretation by experienced pathologists. For 3D digital images, the system achieved a concordance rate of 90% with traditional pathological diagnosis. This system could serve as an integrated plugin onto existing open-source platforms. Appling the system for 3D analysis, the spatial distribution of PD-L1 showed heterogeneity across different layers. Notably, 30% cases crossed the 1% cutoff along the 3D layers, which is related to admission of immunotherapy. The computer-aided AI system could improve the PD-L1 diagnosis from IHC to 3D digital images, which provides accurate prognostic significance for precision medicine.