Comparative Analysis Of Two-Dimensional And Three-Dimensional MRI Radiomics Models For Predicting Molecular Biomakers In Breast Cancer.

BackgroundBreast cancer (BC) incidence continues to rise globally, with diverse clinical presentations that remain challenging to predict. PurposeTo compare the predictive performance of conventional 2D DCE-MRI-based radiomics models with advanced 3D DCE-MRI-based radiomics models for determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), and Ki-67 expression in breast cancer patients. Material and MethodsThis study included 385 patients with histologically confirmed breast cancer. Semi-automatic segmentation was applied to delineate regions of interest (ROIs) on DCE-MRI images, and radiomic features were extracted using Intelligence Foundry (IF) 3.1. Feature selection involved single-value filtering, correlation analysis, outlier optimization, normalization, and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression was used for statistical analysis, and model performance was evaluated using area under the receiver operating characteristic curve (AUC), decision curve analysis (DCA), and DeLongs test for comparing 2D and 3D models. ResultsThe 3D models consistently outperformed 2D models across all biomarkers. In the validation set, the 3D models achieved AUCs of 0.884 for ER, 0.861 for PR, 0.898 for HER-2, and 0.886 for Ki-67, while the 2D models yielded AUCs of 0.813 (ER), 0.740 (PR), 0.821 (HER-2), and 0.796 (Ki-67). DCA demonstrated greater net clinical benefit for 3D models, and DeLongs test revealed statistically significant differences between 2D and 3D models (p < 0.05) for all biomarker groups. DiscussionBoth 2D and 3D DCE-MRI radiomics models demonstrated substantial potential for predicting molecular marker expression in breast cancer. However, 3D models exhibited superior performance, likely attributable to their comprehensive capture of spatial tumor heterogeneity. These findings support the preferential use of 3D radiomics models in future research and clinical practice for non-invasive biomarker assessment.