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Habitat Imaging Fusion Predicts Lymphovascular Invasion in Breast Cancer

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Key Facts

Why It Matters / Context

Lymphovascular invasion (LVI) is a critical prognostic factor in invasive breast cancer, influencing staging, surgical planning, and adjuvant therapy decisions. However, reliable preoperative prediction remains challenging because current non-invasive tools lack accuracy and interpretability. Conventional single-modality radiomics or deep learning approaches often fail to capture intratumoral heterogeneity, limiting their predictive power. This study addresses that gap by integrating habitat imaging—a technique that partitions tumors into biologically distinct subregions—with deep learning features, creating a fusion model that leverages both spatial heterogeneity and high-level pattern recognition. The approach is based on routine B-mode ultrasound, making it widely accessible and cost-effective. By providing a robust, interpretable tool, this method could transform preoperative assessment, allowing clinicians to tailor treatment strategies before surgery.

Details

The study included 1,096 patients from three institutions PMID: 42671326. 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.

The fusion model achieved the following areas under the receiver operating characteristic curve (AUCs):

The fusion model significantly outperformed both the Habitat2 radiomics-only model and the ResNet50-only model (DeLong test, all comparisons significant) PMID: 42671326. Biological interpretability was assessed through correlation analysis with CD31-stained microvessel density, linking the imaging features to underlying vascular biology.

Limitations

• Retrospective design: The study is retrospective, which introduces potential selection bias and limits causal inference.
• Limited generalizability: Although multicenter, the cohort was drawn from only three institutions, and the model’s performance in diverse populations or with different ultrasound equipment remains to be validated.
• Need for prospective validation: Prospective trials are required to confirm clinical utility and impact on patient outcomes.
• Interpretability constraints: While habitat imaging provides some biological insight, the fusion model’s decision-making process is not fully transparent, and the correlation with microvessel density is exploratory rather than a direct clinical validation.
• Radiologist performance data: The abstract mentions comparison with unaided senior and junior radiologists but does not provide their AUCs, limiting a complete assessment of the model’s added value.

Clinical Takeaway

Integrating habitat imaging with deep learning on routine ultrasound can provide a reliable, non-invasive tool for preoperative LVI prediction, potentially improving surgical planning and patient counseling.

Full Reference

Zhong L, Wang K, Xie J, Shi L, Gu L, Zheng Y. A Habitat Imaging-Based Radiomics and Deep Learning Fusion for Preoperative Prediction of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study. Balkan Med J. 2026; PMID: 42671326, DOI: 10.4274/balkanmedj.galenos.2026.2026-7-3.