A Habitat Imaging-Based Radiomics and Deep Learning Fusion for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

Background:Lymphovascular invasion (LVI) is a critical prognostic factor in invasive breast cancer; however, reliable preoperative prediction remains challenging because of the lack of non-invasive and accurate assessment tools. Ultrasound-based radiomics and deep learning have shown promise, but conventional single-modality approaches often fail to capture intratumoral heterogeneity, thereby limiting their predictive performance and clinical interpretability. Aims:To develop and validate a non-invasive ultrasound-based model that integrates habitat radiomic features with deep learning features for the preoperative prediction of LVI in invasive breast cancer and to evaluate whether this model could enhance the diagnostic performance of radiologists. Study Design:Retrospective multicenter diagnostic study. Methods:A total of 1,096 patients from three institutions were included. Tumors on routine B-mode ultrasound were partitioned into three habitats using unsupervised k-means clustering (k = 3). Habitat2, identified as a high-risk subregion associated with LVI, was used for selective radiomic feature extraction. In parallel, deep learning features from all habitat subregions were extracted using a pretrained ResNet50 model. Radiomic and deep learning features were fused at the feature level, and a LightGBM classifier was trained and evaluated in an internal validation cohort and two independent external cohorts. Model performance was compared with that of the Habitat2 radiomics-only and ResNet50-only models using the DeLong test and with the performance of unaided senior and junior radiologists. Biological interpretability was assessed through correlation analysis with CD31-stained microvessel density. Results:The fusion model achieved areas under the receiver operating characteristic curve (AUCs) of 0.918 (training), 0.898 (internal validation), 0.890 (External Cohort 1), and 0.905 (External Cohort 2), significantly outperforming the Habitat2 radiomics-only and ResNet50-only models (DeLong test: fusion vs. Habitat2 radiomics, all p < 0.001; fusion vs. ResNet50, p < 0.001, 0.021, 0.029, and 0.026, respectively). Habitat-specific radiomic and deep learning features showed weak-to-moderate correlations with CD31-stained microvessel density, supporting the biological interpretability of the model. Compared with radiologists, the fusion model achieved higher diagnostic performance than unaided senior and junior radiologists in both external cohorts, and model assistance substantially improved the performance of junior radiologists (AUCs up to 0.865 and 0.905). Conclusion:This interpretable habitat-deep learning fusion approach based on routine ultrasound provides accurate preoperative LVI prediction with robust generalizability across multiple centers and suggests the potential to enhance clinical decision-making by improving radiologist diagnostic performance, particularly for less-experienced radiologists.