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MRI Radiomics for Survival Prediction in Brain Metastases: A Machine Learning Analysis
Hamza Adel Salim1, Evan Calabrese1, Ahmed Naeem1
1From the Department of Neuroradiology (R.E., H.A.Q., S.A., A.M., P.K.), The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Radiology (E.C.), Duke University Medical Center, Durham, NC, USA; Department of Radiology (H.A.S., A.N., M.W.), The University of Texas Medical Branch, Galveston, TX, USA; Department of Radiology (J.D.R.), University of California, San Diego, San Diego, Calif; Division of Computational Pathology (S.B.), Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA; Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indianapolis, IN, USA and Department of Computer Science (S.B.), Luddy School of Informatics, Computing, Engineering, Indiana University, Indianapolis, IN, USA; Medical Research Group, MLCommons, San Francisco, CA, USA.
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.
