An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating...

Area of Science:

• Oncology
• Pathology
• Artificial Intelligence

Background:

• Immune checkpoint inhibitors targeting PD-L1 show promise in urothelial carcinoma (UC).
• The combined positive score (CPS) quantifies PD-L1 22C3 expression but faces inter-pathologist variability.
• Variability stems from assessing both immune and tumor cell positivity.

Purpose of the Study:

• To develop and validate an AI-powered tool for quantifying PD-L1 expression in UC.
• To assess the AI tool's ability to improve consistency in PD-L1 combined positive score (CPS) assessment.
• To evaluate the AI tool's impact on inter-pathologist agreement, particularly in multi-institutional contexts.

Main Methods:

• Developed an AI PD-L1 CPS analyzer using 1,275,907 cells and 6175.42 mm² of pathologist-annotated tissue from 400 UC whole slide images.
• Validated the AI model on 543 UC PD-L1 22C3 cases from three institutions.
• Pathologists re-evaluated discrepancy cases with AI assistance, and agreement rates were compared.

Main Results:

• The AI model achieved 89.5% agreement with the consensus of two or more pathologists.
• Pathologist agreement increased from 82.1% to 93.9% after using the AI as a guide.
• The AI tool mitigated discordance related to hospital source, specimen type, T stage, histology, and PD-L1 cell type.

Conclusions:

• AI models can significantly reduce inter-pathologist discrepancies in quantifying PD-L1 22C3 CPS.
• AI tools are particularly valuable for improving consistency in telepathology and multi-institutional studies.
• This AI solution enhances the reliability of PD-L1 biomarker assessment in urothelial carcinoma.