MRI Radiomics for Survival Prediction in Brain Metastases: A Machine Learning Analysis.
BACKGROUND Brain metastases carry poor prognosis, and accurate survival prediction is critical for treatment planning. Radiomics offers a means of extracting high-dimensional imaging biomarkers, but its prognostic utility in brain metastasis remains unclear. PURPOSE To evaluate whether MRI-derived radiomic features can improve survival prediction in patients with brain metastases. MATERIALS AND METHODS This retrospective study developed a T1 postcontrast MRI radiomics survival model in a public brain metastasis cohort of 198 patients and externally validated the fixed radiomics model, without refitting or recalibration, in an independent cohort of 69 patients. Patient-level radiomics features were derived from lesion-level features aggregated across the three largest lesions. An elastic-net Cox model was selected with 10-fold cross-validation. Model discrimination was assessed with Harrell C-index and 2000 bootstrap resamples for 95% CIs. RESULTS The final model retained 7 nonzero T1 postcontrast radiomics features after correlation filtering and elastic-net selection. The radiomics score had a C-index of 0.615 (95% CI, 0.543-0.687) in the training cohort and 0.574 (95% CI, 0.491-0.658) in external validation. In the external-validation subset with available Graded Prognostic Assessment, the combined radiomics plus Graded Prognostic Assessment model had a C-index of 0.591 (95% CI, 0.509-0.672). CONCLUSION T1 postcontrast MRI radiomics showed limited standalone discrimination for overall survival in patients with brain metastases. These results support cautious use of radiomics as an exploratory imaging biomarker and emphasize the need for integrated prognostic models that include clinical, treatment, molecular, and systemic disease variables.