aceRadiomics and dosiomics in radionecrosis prediction in brain metastasis treated with Stereotactic Radiation Therapy: a machine learning approach
Introduction Stereotactic Radiotherapy plays a main role in Brain Metastases treatment. Radiomics and Dosiomics, coupled with Machine Learning approaches are emerging in radiation oncology as support in clinical decision-making workflow. In this study a machine learning approach was used to predict the late toxicities induced by Stereotactic Radiotherapy including clinical, radiomics and dosiomics features extracted from patients. Materials and methods Lesions contribution, concomitant and/or sequential treatmen…
Machine learning models trained on clinical, radiomics and dosiomics features predicted radionecrosis after stereotactic radiotherapy for brain metastases with ROC-AUC up to 80%, offering decision support that may improve patient-specific treatment and reduce radiotherapy-induced toxicity severity.
Findings are based on a small single-cohort sample of 37 patients / 113 lesions with wide confidence intervals, limiting generalizability and precision of performance estimates.
Evidence
- Peer-reviewedPhysica Medica2026-09-13
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Truvace Impact Record TRV-2026-1098, v1: “Radiomics and dosiomics in radionecrosis prediction in brain metastasis treated with Stereotactic Radiation Therapy: a machine learning approach.” Truvace, 2026-09-15. /record/TRV-2026-1098 (accessed at citation time). sha256 773942fd7fa92269…
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