Abstract PO5-07-02: AI-assisted interpretation of PD-L1 CPS improves the precision medicine in Triple-negative breast cancer
Authors: Jinze Li, Xinran Wang, Yueping Liu
Journal: Cancer Research (2024)

Abstract Objective: The IMpassion 031 and KEYNOTE-355 clinical trial demonstrated the advantages of atezolizumab and pembrolizumab in the treatment of Triple-negative breast cancer (TNBC). However, the application of CPS in assessing PD-L1 expression is challenging. Pathologists have great variability in interpretation of PD-L1 CPS, the daily diagnostic workload is too heavy and their diagnostic efficiency is low, and they still have deficiencies in terms of accurate enumeration. It is of necessity to establish an objective and effective method which is highly repeatable. Methods: In this study, we established a deep learning-based artificial intelligence-assisted (AI-assisted) model which using cell detection and region segmentation algorithm. Three rounds of ring studies (RSs) were conducted. 12 pathologists of different level evaluate the CPS of PD-L1 (DAKO 22C3) in TNBC patients by visual assessment and AI-assisted model. Compare the difference, consistency and accuracy of the interpretation. Our research evaluated PD-L1 CPS expression with continuous score. Shapiro-Wilk (S-W) method performs normality test, difference analysis using Friedman M and Bonferroni calibration tests, consistency analysis is studied by intraclass correlation coefficient (ICC) and heat maps and box plots. Results: In the visual assessment, the interpretation results of PD-L1 (DAKO 22C3) CPS in different level pathologists have significant differences (P &amp;lt; 0.05), and the consistency of all the pathologists interpretation results was weak. Due to this strong inconsistency, it may result in a number of patients losing the opportunity to use ICIs for treatment. Moreover, the internal consistency of all pathologists in the visual assessment is moderate, in which the repeatability of junior pathologists is the worst, the ICC value is 0.664 (95%CI: 0.564-0.762). Through AI-assisted interpretation, there is no significant difference between all pathologists (P = 0.425), and the ICC value increased to 0.883 (95%CI:0.836-0.922) which improved the consistency of the interpretation results. In addition, through AI-assisted interpretation, the repeatability and accuracy of the interpretation results has been further upgraded. At the same time, the acceptance of AI results by junior pathologists are high, and 80% of the AI results are accepted. Conclusions: With the help of the AI-assisted diagnostic model, different levels of all the pathologists have achieved excellent consistency and repeatability in the interpretation of PD-L1 (DAKO 22C3) CPS. Moreover, the level of interpretation has been sought the rapid enhancement. It can be seen that AI-assisted diagnostic model provides a good approach to strengthen the consistency and repeatability in clinical practice. Our research also shows that PD-L1 CPS can receive precise interpretation from pathologists, so more patients have the opportunity to use ICIs. Therefore, these patients can benefit from treatment and achieve better survival. The interpretation results and system diagram of PD-L1 CPS by AI-assisted model. The consistency and repeatability of the interpretation results. (A)-(C) The consistency of the PD-L1 CPS interpretation among the different level of pathologists in three ring studies. (D)The repeatability of the interpretation results between 12 pathologists in RS1 and RS2. Accuracies and acceptance rates of different experienced pathologists. (A) Boxplots of scoring accuracies for pathologists in different levels. (B) Acceptance rates of all pathologists in total 50 slides. (C) Percent stacked column chart of different levels of pathologists with AI score acceptance. Citation Format: Jinze Li, Xinran Wang, Yueping Liu. AI-assisted interpretation of PD-L1 CPS improves the precision medicine in Triple-negative breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-02.