An Update on Machine Learning in Neuro-oncology Diagnostics

TL;DR

This paper reviews the use of machine learning techniques in neuro-oncology diagnostics, highlighting their role in image analysis, tumor classification, and prognosis, while noting current limitations and the need for more robust studies.

Contribution

It provides an updated overview of machine learning applications in neuro-oncology imaging, emphasizing recent advances and ongoing challenges in clinical implementation.

Findings

Machine learning improves tumor classification accuracy.

Advanced imaging techniques aid in molecular and histological profiling.

Current evidence is mostly retrospective and single-center.

Abstract

Imaging biomarkers in neuro-oncology are used for diagnosis, prognosis and treatment response monitoring. Magnetic resonance imaging is typically used throughout the patient pathway because routine structural imaging provides detailed anatomical and pathological information and advanced techniques provide additional physiological detail. Following image feature extraction, machine learning allows accurate classification in a variety of scenarios. Machine learning also enables image feature extraction de novo although the low prevalence of brain tumours makes such approaches challenging. Much research is applied to determining molecular profiles, histological tumour grade and prognosis at the time that patients first present with a brain tumour. Following treatment, differentiating a treatment response from a post-treatment related effect is clinically important and also an area of study. Most of the evidence is low level having been obtained retrospectively and in single centres.