1144P When AI-based image analysis gets in clinical trials

Background: Especially during the COVID-19 pandemic, implementation and development of whole slide scanning lead to an active growth of digital pathology and image analysis (IA). In the present study, we retrospectively conducted a global evaluation of three Breast cancer markers: ER, PR and Ki67 with the aim to study the correlation between pathologist conventional semi quantitative scoring method on glass and scanned slides versus artificial intelligence-based IA. Methods: Study samples were scored independently either by five independent pathologists on scanned images and glass slides, or using supervised IA algorithms (IA results validated by pathologists). The readout for the three markers was the percentage of tumor positives stained cells. The correlation between the pathologist evaluation on glass slides versus scanned images was calculated using Pearson’s correlation coefficient. Pathologist’s evaluations and IA results were compared using Intraclass Correlation Coefficient (ICC). Additionally, the average time spent by the pathologist per sample was measured for each evaluation method. Results: The correlation of pathologist evaluation between glass slide and scanned image showed a Pearson’s correlation coefficient ≥ 0.90 for each marker. The ICC between IA algorithm and pathologist was on average over 0.8 for the three markers, showing a good agreement between the different scoring method. However, some challenges were identified related to the detection of tumor area that needed some additional pathologist review for specific complex cases. Overall, time required by the pathologist for a complete evaluation decreased by roughly 3 times when supported by image analysis tools. Conclusions: Based on the Pearson’s correlation coefficient and the ICC results, we observe an equivalence in the pathologist conventional scoring (Image or glass slides) and the use of IA. In an era where regulations are still being discussed for the use of algorithm by the FDA (AI-Based or not), we can mitigate regulatory requirements by having pathologists reviewing the results of a digital analysis. We conclude here to a benefit from the combination of pathologist evaluation and IA in terms of time with at least equivalent results in terms of accuracy. Legal entity responsible for the study: Covance. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.