Intratumoral Heterogeneity-aware Imaging Biomarkers for Bone Metastasis Risk Stratification in Breast Cancer
Liwei Sun1, Weizhi Zhang2, Zhengtong Wang3
1Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China (L.S., P.N., W.X.); Qingdao Medical College, Qingdao University, Qingdao, China (L.S.).
Rationale And Objectives:
Breast cancer carries a substantial long-term risk of bone metastasis, which marks progression to incurable disease and severely compromises patient quality of life. Conventional clinicopathological factors provide limited individualized prognostic resolution, highlighting the need for noninvasive biomarkers capable of capturing tumor biology relevant to bone-specific metastatic progression.
Materials And Methods:
We systematically compared two commonly used intratumoral heterogeneity (ITH) modeling strategies: feature-level heterogeneity encoding based on tumor radiomics features complexity (ITH1) and habitat-based regional aggregation derived from supervoxel partitioning (ITH2). Prognostic performance for bone metastasis-free survival (BMFS) was also evaluated by global tumor region (GTR) radiomics and vision transformer (ViT)-derived deep learning features. Multidimensional models integrating imaging-derived scores and clinicoradiological variables were constructed and interpreted using SHapley Additive exPlanations (SHAP).
Results:
ITH1 consistently outperformed ITH2 in prognostic performance and generalizability, indicating greater robustness in capturing tumor heterogeneity relevant to bone metastasis. Integration of the ITH1 score with GTR radiomics and ViT scores significantly improved BMFS prediction compared with any single modality. The final integrated model incorporating clinicoradiological variables achieved high and stable discrimination across cohorts, with concordance index values ranging from 0.878 to 0.908. SHAP analysis revealed that the ITH1 score contributed most prominently to bone metastasis risk prediction, underscoring the critical role of tumor heterogeneity in bone-specific metastatic risk.
Conclusion:
Feature-level ITH encoding provides a more informative and robust representation of ITH than habitat-based modeling for predicting breast cancer bone metastasis. Multidimensional integration of ITH, GTR radiomics, and ViT-derived features enables complementary tumor characterization and substantially enhances individualized risk stratification, supporting its potential utility in precision oncology.
