MRI-based Habitat Analysis for the Prediction of Progression-Free Survival in Primary Spinal Tumors

Background Prediction of prognosis in patients with primary spinal tumors is critical for treatment planning but a clinical challenge. Purpose To develop an MRI-based nested habitat radiomics model to predict progression-free survival (PFS) in patients with primary spinal tumors. Materials and Methods This dual-center retrospective study analyzed patients with resected primary spinal tumors. Patients from March 2010 to August 2019 were assigned to a training set; from September 2019 to October 2022, to an internal test set; and from March 2010 to October 2022, to an external test set. Whole-tumor regions of interest were delineated on T1- and T2-weighted MRI scans. Nested habitat radiomic analysis was performed to locate aggressive subregions associated with a poorer prognosis. Initially, a support vector machine model classified tumors based on global features. Then, local radiomics features were extracted to generate probability maps to identify aggressive tumor subregions with use of <i>k</i>-means clustering. Features from tumor microregions with the highest probability were used to build a final habitat model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and Kaplan-Meier survival analysis. Results Among 259 patients (median age, 39 years [IQR, 28-53 years]; 150 female), the habitat model better predicted PFS compared with the whole-tumor radiomics model (AUC [training set], 0.93 [95% CI: 0.87, 0.98] vs 0.82 [95% CI: 0.68, 0.93]; <i>P</i> = .03, DeLong test). The habitat model combined with clinical characteristics showed the best performance (AUC [training set], 0.95 [95% CI: 0.88, 0.99]; AUC [internal test set], 0.86 [95% CI: 0.69, 0.96]; AUC [external test set], 0.89 [95% CI: 0.76, 0.96]). The nested habitat radiomics score was an independent risk factor for 3-year PFS (high- vs low-risk group PFS: 24 vs 28 months; log-rank <i>P</i> = .04) in the external test dataset. Conclusion In patients with primary spinal tumors, an MRI-based nested habitat radiomics model outperformed other models in predicting PFS. © RSNA, 2025 <i>Supplemental material is available for this article.</i> See also the editorial by Jabehdar Maralani and Burgess in this issue.