Frontiers in oncology · 2026
Habitat imaging based on DCE-MRI for differentiating luminal and non-luminal subtypes of breast cancer: a two-center study.
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Qing Zhang, Xiao-Tao Qin, Xia Zhou, Xiu-Lan Zhang, Jie Peng, Yun Li, Wei-Bing Wang
Аннотация
OBJECTIVE: To explore the application value of habitat imaging based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for differentiating luminal and non-luminal subtypes of breast cancer (BC).
METHODS: Retrospective data from 396 BC patients across two centers were collected. The data from Center 1 were split into a training set of 220 patients and an internal validation set of 56 patients, while Center 2 provided an external test set of 120 patients. Multivariable analysis was performed to identify independent risk factors for developing the clinical model. K-means algorithm was used to perform clustering on DCE-MRI. After feature extraction and selection, eight machine learning algorithms were utilized to build traditional radiomics model, habitat model, and clinical model. A stacking fusion strategy was employed to integrate the traditional radiomics model, habitat model, and clinical model for identifying luminal and non-luminal subtypes patients. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curve and decision curve analysis (DCA). Shapley Additive Explanations (SHAP) was performed for model interpretability.
RESULTS: For the discrimination of breast cancer luminal and non-luminal subtypes, the stacking model yielded the largest area under the curve value (AUC = 0.840), followed by the habitat model and the conventional radiomics model (AUC = 0.830, AUC = 0.805, respectively), all of which were significantly better than the clinical model (p < 0.05, respectively). Calibration curves showed good calibration of the stacking model, and decision curves confirmed its favorable net clinical benefit. SHAP revealed habitat-LGBM in the stacking model with their contribution being particularly prominent.
CONCLUSION: Habitat imaging exhibits promising potential to differentiate luminal and non-luminal breast cancer subtypes. The stacking model integrating habitat-LGBM, traditional radiomics-LGBM and clinical-XGBoost may provide favorable predictive performance.
METHODS: Retrospective data from 396 BC patients across two centers were collected. The data from Center 1 were split into a training set of 220 patients and an internal validation set of 56 patients, while Center 2 provided an external test set of 120 patients. Multivariable analysis was performed to identify independent risk factors for developing the clinical model. K-means algorithm was used to perform clustering on DCE-MRI. After feature extraction and selection, eight machine learning algorithms were utilized to build traditional radiomics model, habitat model, and clinical model. A stacking fusion strategy was employed to integrate the traditional radiomics model, habitat model, and clinical model for identifying luminal and non-luminal subtypes patients. Model performance was evaluated using receiver operating characteristic (ROC) curves, calibration curve and decision curve analysis (DCA). Shapley Additive Explanations (SHAP) was performed for model interpretability.
RESULTS: For the discrimination of breast cancer luminal and non-luminal subtypes, the stacking model yielded the largest area under the curve value (AUC = 0.840), followed by the habitat model and the conventional radiomics model (AUC = 0.830, AUC = 0.805, respectively), all of which were significantly better than the clinical model (p < 0.05, respectively). Calibration curves showed good calibration of the stacking model, and decision curves confirmed its favorable net clinical benefit. SHAP revealed habitat-LGBM in the stacking model with their contribution being particularly prominent.
CONCLUSION: Habitat imaging exhibits promising potential to differentiate luminal and non-luminal breast cancer subtypes. The stacking model integrating habitat-LGBM, traditional radiomics-LGBM and clinical-XGBoost may provide favorable predictive performance.