Prediction of Ki-67 expression in invasive breast cancer with dual-modality radiomics

Background Ki-67 expression, a critical biomarker for tumor aggressiveness and proliferation in invasive breast cancer, is traditionally assessed via invasive biopsies, which suffer from sampling variability and limit serial monitoring. This study aimed to develop radiomics models using ultrasound (US) and mammography (MG) features to predict Ki-67 expression, hypothesizing that dual-modality integration improves accuracy over single-modality approaches. Methods A retrospective study was performed on consecutive patients diagnosed with invasive breast cancer at Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine from January 2017 to May 2024. Radiomic features were extracted from US and MG images using Pyradiomics and refined via least absolute shrinkage and selection operator (LASSO) regression.The dataset was initially split into training and test sets at 7:3 ratio. Support vector machine were constructed and then validated via three-round 5-fold cross-validation. The key features were identified using LASSO and Shapley Additive exPlanations (SHAP) analysis. Clinical utility was assessed using decision curve analysis (DCA). Results High Ki-67 expression (> 20%) was significantly associated with higher histologic grade (Grade III: 76.9% vs 23.5%, p < 0.001), larger tumor size (2.1–5.0 cm: 61.1% vs 41.8%, p = 0.019), ER/PR negativity (p < 0.001), HER2 positivity (p = 0.010), and aggressive molecular subtypes (Luminal B, HER2+, Triple-negative; p < 0.001). After feature selection, 12 US, 17 MG, and 42 combined features were retained. The combined model significantly outperformed single-modality models in test-set 3-round 5-fold cross-validation, achieving the highest area under the curve (0.882 vs. 0.748 for US and 0.771 for MG, p < 0.05), with balanced sensitivity (83.0 ± 9.3%) and specificity (73.0 ± 7.6%). SHAP analysis identified texture-based features (e.g., us_Square_glszm_SizeZoneNonUniformityNormalized) as critical predictors. DCA demonstrated the combined model offered the greatest net benefit across threshold probabilities (20–60%), confirming its superior clinical utility. Conclusions Integrating US and MG radiomics significantly improves the non-invasive prediction of Ki-67 expression in BC. This approach complements biopsy by enabling serial monitoring of tumor proliferation, with potential to inform personalized treatment but requires multi-center validation to enhance generalizability.