1220P - Comprehensive diagnose of programmed death-ligand 1 from two-dimensional to three-dimensional in breast cancer with computer-aided artificial intelligence system

Background

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.

Methods

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.

Results

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.

Conclusions

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.

Clinical trial identification

Editorial acknowledgement

Legal entity responsible for the study

The authors.

Funding

Has not received any funding.

Disclosure

All authors have declared no conflicts of interest.