Clinical evaluation of an automated pan-organ combined PD-L1 scoring using artificial intelligence on immunostained whole-slide images

Background

Programmed death-ligand 1 (PD-L1) inhibitors have shown remarkable results in oncology; however, many patients fail to respond, highlighting the need for reliable assessment of PD-L1 expression for patient selection. PD-L1 scoring, especially the combined positive score (CPS), is hindered by inter- and intraobserver variability, complex staining patterns, and technical discrepancies, all of which can impact therapeutic decisions. Artificial intelligence (AI) offers a solution by standardizing PD-L1 evaluation. This study evaluates a PD-L1 CPS AI, designed for reproducible and robust PD-L1 scoring across various tumor types and conditions.

Materials and methods

AI performance was validated on 142 samples spanning multiple tumor types (gastrointestinal, head and neck, breast, and uterine cervix) and sourced from four centers, reflecting diverse staining protocols. Routine scores were available. A gold standard was established through independent retrospective scoring by three senior pathologists enabling the assessment of variability. The scoring process was followed by collegial discussions to resolve discordant cases and ensure medical consensus. After a washout period, cases were reassessed with AI assistance. AI and routine manual scores were compared with the gold standard using organ-specific cut-offs.

Results

AI assistance improved interobserver agreement among pathologists, increasing intraclass correlation coefficient (ICC) from 62% to 74%, with a particularly pronounced effect in challenging cases with CPS < 20 (n = 91), where ICC improved from 19% to 62%, underscoring the value of AI in reducing variability near clinical decision thresholds. Based on clinical cut-offs, AI-based scoring outperformed routine manual scoring in accuracy (88% versus 75%) and sensitivity (96% versus 78%), while maintaining a comparable positive predictive value (88% versus 87%), indicating an improved ability to detect true-positive cases.

Conclusions

This study highlights the potential of an AI-driven tool—DiaKwant PD-L1 algorithm—to improve PD-L1 scoring accuracy and reduce observer variability, particularly near clinical thresholds, across various solid carcinomas, independently of pre-analytical and digitization platforms. Its integration into clinical workflows could enhance efficiency and optimize patient eligibility for immunotherapy.