A BART machine learning model to classify lipomatous tumors in MR images

Motivation: Atypical lipomatous tumors (ALTs) neoplasms are distinguished from benign lipomas primarily through histopathology. MRI alone has variable effectiveness in distinguishing ALTs from lipomas. Machine learning has shown promise in this differentiation. Goal(s): This study introduces the Bayesian additive regression trees (BART) model for non-invasive ALT classification, leveraging Bayesian priors to enhance model robustness. Approach: Retrospective data was collected from 5 medical institutions, 437 patients where used to train the model. Results: A Bayesian additive regression trees model, built from MRI radiomic features, performed comparable to an experienced radiologist for classifying lipomatous tumors. Impact: This method can assist radiologists in screening for ALT in regions with low incidence rates. By using machine learning less biopsies' will be needed. We would like to implement this model in other detection tasks such as brain tumor classification.