ArticleNeuro-oncology2024
Radiomics-based prediction of local control in patients with brain metastases following postoperative stereotactic radiotherapy.
Article in Neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of artificial intelligence for brain tumor segmentation, treatment planning, and outcome prediction: a comprehensive systematic review and meta-analysis.Journal of neuro-oncology · 2025Pooled it
- Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models.European radiology · 2026Article
- Mechanisms, Imaging Phenotypes, and Therapeutic Advances of Neovascularization in Brain Metastases.Biomedicines · 2026Review
- Development and application of a prognostic model based on radiomics and artificial intelligence for patients with lung adenocarcinoma brain metastasis.Frontiers in oncology · 2026Article
- Multimodal DeepSurv model with SHAP-based interpretation for predicting local failure in patients with brain metastases undergoing hypofractionated stereotactic radiotherapy.Frontiers in neuroscience · 2026Article
- Image-Guided Radiooncology: The Potential of Artificial Intelligence in Clinical Application.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- The effect of postoperative chemotherapy on survival outcomes and a nomogram for predicting overall survival in chondroblastic osteosarcoma.Scientific reports · 2025Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Hybrid Deep Learning for Survival Prediction in Brain Metastases Using Multimodal MRI and Clinical Data.Diagnostics (Basel, Switzerland) · 2025Article
- Development of Clinical-Radiomics Nomogram for Predicting Post-Surgery Functional Improvement in High-Grade Glioma Patients.Cancers · 2025Article
- Machine learning to predict radiomics models of classical trigeminal neuralgia response to percutaneous balloon compression treatment.Frontiers in neurology · 2024Article
- GlioMODA: Robust glioma segmentation in clinical routine.Neuro-oncology advancesArticle
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30 authors.
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Abstract
backgroundSurgical resection is the standard of care for patients with large or symptomatic brain metastases (BMs). Despite improved local control after adjuvant stereotactic radiotherapy, the risk of local failure (LF) persists. Therefore, we aimed to develop and externally validate a pre-therapeutic radiomics-based prediction tool to identify patients at high LF risk.
methodsData were collected from A Multicenter Analysis of Stereotactic Radiotherapy to the Resection Cavity of BMs (AURORA) retrospective study (training cohort: 253 patients from 2 centers; external test cohort: 99 patients from 5 centers). Radiomic features were extracted from the contrast-enhancing BM (T1-CE MRI sequence) and the surrounding edema (T2-FLAIR sequence). Different combinations of radiomic and clinical features were compared. The final models were trained on the entire training cohort with the best parameter set previously determined by internal 5-fold cross-validation and tested on the external test set.
resultsThe best performance in the external test was achieved by an elastic net regression model trained with a combination of radiomic and clinical features with a concordance index (CI) of 0.77, outperforming any clinical model (best CI: 0.70). The model effectively stratified patients by LF risk in a Kaplan-Meier analysis (P < .001) and demonstrated an incremental net clinical benefit. At 24 months, we found LF in 9% and 74% of the low and high-risk groups, respectively.
conclusionsA combination of clinical and radiomic features predicted freedom from LF better than any clinical feature set alone. Patients at high risk for LF may benefit from stricter follow-up routines or intensified therapy.
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