Machine learning-based MRI radiomics to predict postoperative complications following peripheral nerve sheath tumour excision.

Abstract

This study sought to establish and validate a machine learning-based multi-sequence MRI radiomics model for predicting postoperative complications in patients with peripheral nerve sheath tumours. We conducted a retrospective analysis of 303 patients with pathologically confirmed tumours, extracting features from <i>T</i>₁-weighted and <i>T</i>₂-weighted MRI scans. Relevant radiomic features were identified through interclass correlation coefficient analysis, <i>t</i>-tests and least absolute shrinkage and selection operator techniques. A multi-sequence radiomics model was developed using the Light Gradient Boosting Machine classifier, alongside a clinical-radiomics model that incorporated clinical features. The models exhibited robust diagnostic performance, with areas under the receiver operating characteristic curve reaching 0.95 in the training cohort. These findings underscore the model's potential to accurately predict postoperative complications, providing crucial support for clinicians in devising personalized treatment strategies for patients with peripheral nerve sheath tumours.<b>Level of evidence:</b> Prognostic III.