Bayesian additive regression trees for machine learning to classify benign vs atypical lipomatous tumors on MRI.

ABSTRACT:

Background

Atypical lipomatous tumors (ALTs) are aggressive fat cell tumors that are distinguished from benign lipomas (SL) mainly through histopathology. Biopsy is needed for suspicious cases but can miss malignancy, so complete surgical removal and examination are essential. MRI is used but often can't differentiate ALT from SL. We introduce a machine learning method for tumor classification.

Purpose

To characterize the classification performance of a Bayesian additive regression trees (BART) model, built from MR radiomic features, and compare it to the readings of a musculoskeletal radiologist in classifying atypical lipomatous tumors (ALTs) from simple lipomas.

Materials and methods

Retrospective data were collected from 5 medical institutions in North America, fo

SUBMITTER: Godinez F 

PROVIDER: S-EPMC12548370 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature