A number of immunotherapy treatments in cancer can be guided by a patient's tumor mutation burden (TMB) status. However typical TMB determination is performed via next generation sequencing which is problematic due to its high cost, low availability worldwide and operational challenges. The ability to screen patients for TMB determination using alternative methodologies is of interest, particularly in the use of H&E slides which are readily available for most cancer patients. Bio-AI Health has developed a machine learning (ML) classifier model capable of identifying TMB status in patients using H&E slides only. Our automated model is unique in that it uses an enriched signal from samples consisting of tumor region only while generating unique visualizers that provide an insight into the performance and mode of action of the model itself. This visualizer allows the pathologist a unique view into the ML performance therefore eliminating the black box concept and providing a crucial QC step to ensure proper algorithm performance. This test can increase the sensitivity of detection providing significant clinical utility. The dataset for model development was obtained from TCGA cohort and TMB status was calculated using whole exome sequencing data. We further developed automated ML segmentation models to accurately segment the tissue into multiple compartments and filter our all necrotic and non-neoplastic tissue regions. Taking into account only tumor regions, the classifier ML model uses an ensemble approach which integrates the prediction of multiple convolutional network probabilities and achieves an accuracy of 0.76 with a sensitivity of .79 and F1 score of .82 towards the classification of the tissue as TMB high or Low.
Development of a TMB classifier on NSCLC H&E images