The study, published in BMC Medical Imaging, was a retrospective dual-center investigation led by Yi Dai of Peking University Shenzhen Hospital together with colleagues at Guangdong Provincial People’s Hospital. The researchers enrolled 156 patients who had undergone pretreatment dynamic contrast-enhanced MRI, the standard contrast-enhanced breast imaging technique, and who had also received MammaPrint testing on their surgical or biopsy specimens. Their goal was ambitious: to see whether the subtle texture patterns hidden inside routine clinical scans could stand in for a molecular assay that requires actual tumor tissue.

The approach rested on two complementary computational techniques. The first is radiomics, a method that converts medical images into hundreds of quantitative features describing shape, intensity, and texture characteristics that the human eye cannot reliably perceive. The second is habitat analysis, a technique that acknowledges a fundamental biological truth: tumors are not uniform. Instead of treating a tumor as a single lump of tissue, habitat imaging partitions it into distinct subregions, or habitats, based on how different areas take up contrast agent on MRI. These subregions are thought to reflect genuine biological differences, such as variations in cell density, blood supply, and oxygen levels, that drive aggressive behavior.

Crucially, the team did not stop at the tumor’s visible border. They extracted radiomic features from both the intratumoral region, the tumor itself, and the peritumoral region, the surrounding tissue that radiologists typically discard. This decision reflects growing evidence that the tumor microenvironment, including the stromal reaction and immune infiltration in adjacent tissue, carries information about a cancer’s biology. By combining features from inside and outside the tumor with habitat-based heterogeneity measures, the researchers built what they call a combined radiomics-habitat model.

Feature selection followed a rigorous pipeline. Radiomic features were standardized according to the Image Biomarker Standardisation Initiative, an international effort to make radiomic features reproducible across centers and scanners. The team then applied the least absolute shrinkage and selection operator, known as LASSO, together with minimum redundancy maximum relevance filtering to whittle down the initial feature set to the most informative and least redundant variables. Multiple machine learning classifiers were then trained to distinguish MammaPrint low-risk from high-risk tumors, and their performance was evaluated with receiver operating characteristic analysis, calibration plots, and decision curve analysis.

The ExtraTrees classifier, an ensemble method that builds many randomized decision trees and averages their votes, emerged as the strongest performer. The combined model achieved an area under the receiver operating characteristic curve of 0.931 in the training set, 0.896 in the internal validation set, and 0.877 in the external test set drawn from the second hospital. An AUC of 0.877 on unseen external data is particularly noteworthy, because it demonstrates that the model’s performance was not an artifact of overfitting to a single institution’s scanner or patient population.

Calibration, which measures whether the model’s predicted probabilities match actual observed risk, was also strong. Hosmer-Lemeshow tests yielded P values of 0.267, 0.491, and 0.892 across the training, internal validation, and external test sets respectively, indicating no statistically significant departure from ideal calibration. Decision curve analysis, a technique that quantifies the net clinical benefit of acting on a model’s predictions across a range of risk thresholds, confirmed that using the model would add value compared with treating all patients the same way or treating none.

The analysis also identified two independent clinical predictors. Mass margin, the boundary characteristic of the tumor on imaging, carried an odds ratio of 0.854, while tumor size carried an odds ratio of 1.014, both statistically significant. In plain terms, tumors with more irregular margins and larger dimensions were more likely to fall into the high-risk genomic category, which aligns with established understanding that infiltrative growth patterns and tumor burden correlate with aggressive biology.

The implications extend beyond a single genomic assay. MammaPrint’s clinical utility was established in the landmark MINDACT trial, and it is incorporated into guidelines from the National Comprehensive Cancer Network and other bodies for guiding chemotherapy decisions in node-negative and select node-positive breast cancer. Yet genomic testing requires adequate tumor tissue, adds cost, and introduces turnaround delays. An imaging surrogate that could be computed from MRI scans already acquired during diagnostic workup would allow risk stratification to begin earlier, potentially informing surgical planning and neoadjuvant treatment discussions before pathology and genomics are finalized.

The authors caution that this remains a retrospective study in hormone receptor-positive, HER2-negative disease, the population in which MammaPrint is most commonly used, and that prospective validation in larger and more diverse cohorts will be needed before clinical deployment. Still, the convergence of habitat imaging, standardized radiomics, and interpretable machine learning represents a tangible step toward what the researchers describe as a noninvasive imaging surrogate for genomic risk stratification. If future trials confirm these results, the MRI scan a patient already receives could quietly become the first word in deciding whether chemotherapy is worth its toll.

Subject of Research: Noninvasive prediction of MammaPrint genomic risk in breast cancer using MRI-based radiomics and habitat analysis

Article Title: Prediction of mammaprint risk in breast cancer using a combined radiomics-habitat model integrating intratumoral and peritumoral MRI features: a retrospective dual-center study

Article References: Dai, Y., Lin, J., Chen, K., Lian, C., Aishanjiang, D., Li, G., Chen, M., Lu, Y., Fang, Z., Cui, J., & Wu, L. (2026). Prediction of mammaprint risk in breast cancer using a combined radiomics-habitat model integrating intratumoral and peritumoral MRI features: a retrospective dual-center study. BMC Medical Imaging. https://doi.org/10.1186/s12880-026-02862-7

Image Credits: AI Generated

DOI: 10.1186/s12880-026-02862-7

Keywords: breast cancer, MammaPrint, radiomics, habitat imaging, MRI, machine learning, tumor heterogeneity, ExtraTrees, peritumoral features, genomic risk stratification, DCE-MRI, predictive biomarkers