Identifying lung imaging biomarkers of BCG vaccination after infection with Mycobacterium tuberculosis

Individual factors that lead to susceptibility to Mycobacterium tuberculosis infection among humans and animal models are not clearly defined. As a result, clinicians and scientists have little ability to diagnose and prognose the various clinical manifestations of tuberculosis, from life-long control of latent infection to active tuberculosis disease. Given the challenges in accurately predicting disease outcomes, vaccination with M. bovis Bacille Calmette-Guerin (BCG) vaccine is used globally in children to prevent systemic disease and tuberculous meningitis. However, in adults, epidemiological studies show variable protection ranging from 0% to 80%. As a part of a larger study to undercover the genomic and transcriptomic factors contributing to this variable efficacy, here we present a deep learning approach to identify mice which have been BCG-vaccinated from those that have not been vaccinated from hematoxylin and eosin stained lung sections of experimentally infected inbred mice. In a leave-one-out cross-validation of 59 slides, our method not only demonstrates ability to identify vaccinated mice with 93% accuracy and non-vaccinated mice with 100% accuracy. Through association with genomic and transcriptomic factors, we envision creating a blueprint for modifying and improving current vaccine strategies.