Habitat-informed MRI Radiomics and Deep Learning Fusion for Short-Term Survival Prediction in Patients with
Hongbo Zhang1, Beibei Zhou2, Siqing Jing1
1Medical Image Center, Shenzhen Hospital, Southern Medical University (Shenzhen School of Clinical Medicine, Southern Medical University), Shenzhen, Guangdong, China (H.Z., S.J., X.Z., Z.Y., J.W., Y.L.); Shenzhen School of Clinical Medicine, Southern Medical University (Shenzhen Hospital, Southern Medical University), Shenzhen, Guangdong, China (H.Z., S.J., X.Z., Z.Y., J.W., Y.L.).
Rationale And Objectives:
To develop and externally validate a preoperative multicontrast MRI stacking model integrating unsupervised habitat radiomics and deep learning to predict short-term survival (overall survival ≤ 9 months) in glioblastoma, and to explore transcriptomic and immune correlates.
Materials And Methods:
Retrospective multicenter study of 468 patients (training, n=196; external validation cohorts 1-3, n=66, 185, and 21). Radiomics models derived from whole-tumor, MRI-defined subregions, and unsupervised habitats were compared with a 3D ResNet-50 model. A two-level stacking ensemble combined habitat radiomics and ResNet-50 predictions. Performance and clinical utility were evaluated by area under the receiver operating characteristic curve (AUC), decision curve analysis, and related metrics. In external cohort 3 with paired RNA sequencing, patients were stratified by fusion-model predictions for exploratory differential expression and immune deconvolution, followed by Spearman correlation analyses.
Results:
Short-term survival occurred in 159/468 (34.0%). Habitat radiomics outperformed whole-tumor and MRI-defined subregion radiomics in external cohorts (AUC, 0.722-0.761), while 3D ResNet-50 achieved AUCs of 0.711-0.739. The fusion model achieved an AUC of 0.974 in the training cohort and AUCs of 0.860, 0.861, and 0.789 in external validation cohorts 1-3, respectively, and yielded the highest net benefit across clinically relevant thresholds. In the RNA-sequenced cohort, DLK1 expression was positively correlated with inferred T follicular helper cells and M1 macrophages.
Conclusion:
Habitat-informed fusion of radiomics and deep learning enables preoperative prediction of short-term survival in glioblastoma. Radiogenomic analyses in the paired cohort are exploratory and suggest a DLK1-related immune-infiltration correlate of the model-defined phenotype.