Tailored Intraoperative MRI Strategies in High-Grade Glioma Surgery: A Machine Learning-Based Radiomics Model Highlights Selective Benefits.
Martin AichholzerPhilip RauchLucia KastlerJosef PichlerKathrin Aufschnaiter-HiessböckFrancisco Ruiz-NavarroStefan AspalterSaskia HartlWolfgang SchimettaPetra BöhmIlja ManakovWolfgang ThomaeMatthias GmeinerAndreas GruberHarald StefanitsPublished in: Operative neurosurgery (Hagerstown, Md.) (2023)
Our machine learning-driven radiomics approach predicts scenarios where 5-ALA alone may be suboptimal in HGG surgery compared with its combined use with iMRI. Although 5-ALA typically yields favorable results, our analyses reveal that HGGs characterized by significant volume, complex morphology, and left-sided location compromise the effectiveness of resections relying exclusively on 5-ALA. For these intricate cases, we advocate for the continued relevance of iMRI.
Keyphrases
- machine learning
- minimally invasive
- high grade
- coronary artery bypass
- contrast enhanced
- lymph node metastasis
- magnetic resonance imaging
- artificial intelligence
- randomized controlled trial
- surgical site infection
- climate change
- systematic review
- low grade
- genome wide
- deep learning
- computed tomography
- squamous cell carcinoma
- patients undergoing
- single cell
- dna methylation
- acute coronary syndrome
- diffusion weighted imaging
- percutaneous coronary intervention