Machine Learning for Digital Scoring of PRMT6 in Immunohistochemical Labeled Lung Cancer

SIMPLE SUMMARY: We trained and validated the machine learning digital scoring method of PRMT6 protein expression in lung cancer tissue samples. PRMT6 is an important biomarker for the progression of lung cancer; however, conventional pathologists’ manual scoring of its expression in large samples is time consuming, particularly when analyzing large sections of lung cancer tissue. Using HALO software, we optimized the digital method for scoring PRMT6 expression on immunohistochemically stained lung cancer tissue. Our optimized digital scoring showed excellent concordance with two pathologists using the immunoreactive scoring method. Our findings showed that digital scoring trained by pathologists can be a more efficient method with a high level of accuracy. ABSTRACT: Lung cancer is the leading cause of cancer death in the U.S. Therefore, it is imperative to identify novel biomarkers for the early detection and progression of lung cancer. PRMT6 is associated with poor lung cancer prognosis. However, analyzing PRMT6 expression manually in large samples is time-consuming posing a significant limitation for processing this biomarker. To overcome this issue, we trained and validated an automated method for scoring PRMT6 in lung cancer tissues, which can then be used as the standard method in future larger cohorts to explore population-level associations between PRMT6 expression and sociodemographic/clinicopathologic characteristics. We evaluated the ability of a trained artificial intelligence (AI) algorithm to reproduce the PRMT6 immunoreactive scores obtained by pathologists. Our findings showed that tissue segmentation to cancer vs. non-cancer tissues was the most critical parameter, which required training and adjustment of the algorithm to prevent scoring non-cancer tissues or ignoring relevant cancer cells. The trained algorithm showed a high concordance with pathologists with a correlation coefficient of 0.88. The inter-rater agreement was significant, with an intraclass correlation of 0.95 and a scale reliability coefficient of 0.96. In conclusion, we successfully optimized a machine learning algorithm for scoring PRMT6 expression in lung cancer that matches the degree of accuracy of scoring by pathologists.