ISMRM: Machine learning plus MRI radiomics estimates brain age

SINGAPORE - Using machine learning with MRI radiomics features accurately assesses brain aging, according to research presented May 6 at the International Society of Magnetic Resonance in Medicine (ISMRM) meeting.Presenter Eros Montin, MD, of New York University Grossman School of Medicine in New York City  reported that a machine learning model using radiomics features from T1- and T2-weighted MR images estimated adult subjects' age with a mean absolute error value of 4.7 years.The study results could translate to improved clinician understanding of brain changes caused by both healthy aging and those caused by neurodegeneration, Montin noted."A machine learning model capable of accurately estimating brain age could have a large clinical impact," he said.Previous research on predicting brain age using structural imaging has shown mean absolute error values of between five and seven years, while studies that combined structural and functional imaging information have shown mean absolute error values of less than four years. But functional imaging -- such as functional or diffusion-weighted MRI -- may not be widely available, and prediction accuracy is reached only with large amounts of data (that is, more than 23,000 cases).That's where MRI radiomics come in. Culling quantitative features from MRI exams "has emerged as a powerful tool for improving patient outcomes and advancing precision medicine," Montin said."Radiomics extracts image features from specific regions of in...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: MRI ISMRM 2024 Source Type: news