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

LVI describes the presence of tumor cell clusters inside endothelial-lined lymphatic vessels or blood vessels surrounding an invasive breast cancer. It is an independent predictor of local recurrence, distant metastasis, and poor prognosis, yet confirming it today depends entirely on postoperative pathology. Surgeons learn whether tumor cells had already entered vessels only after the tumor has been removed, which limits their ability to fine-tune the extent of axillary lymph node dissection or to personalize adjuvant treatment decisions. A reliable preoperative, noninvasive estimate of LVI status has therefore been a long-standing unmet need in breast oncology.

The new study tackles that gap using dynamic contrast-enhanced magnetic resonance imaging, or DCE-MRI, already a core preoperative modality thanks to its exceptional soft-tissue resolution and its sensitivity to tumor vascularization. The researchers combined DCE-MRI with radiomics, a technique that extracts large numbers of quantitative image features invisible to the human eye, and with machine learning, allowing algorithms to decode tumor heterogeneity from routine scans. While earlier efforts have used mammography-based tomosynthesis or single-center MRI cohorts, systematic comparisons of many algorithms on multicenter DCE-MRI data, with rigorous external validation, remained scarce.

Retrospectively, the team assembled 912 female patients aged 23 to 81 years from two independent medical centers, Guangdong Maternal and Child Health Hospital and The First Affiliated Hospital of Jinan University, all scanned with a Philips 3.0T scanner within two weeks before surgery. Data from 757 patients at the first center formed the model development set, split 7:3 into training and internal validation subsets using stratified sampling on LVI status. Data from 155 patients at the second center served as a completely unseen external test set, a design that directly probes how well the model generalizes to a different hospital population rather than merely memorizing patterns from its own data.

The radiomics pipeline began with manual, blinded contouring of the entire tumor on the arterial phase of the six-phase contrast sequence, producing a three-dimensional volume of interest reviewed by senior radiologists. Using PyRadiomics, the team extracted 1,197 features per patient, spanning 234 histogram, 14 morphological, 884 texture, and 65 higher-order descriptors. A multistep reduction process followed: correlation filtering removed redundant features, univariate analyses retained only those significantly associated with LVI, and LASSO regression with 10-fold cross-validation ultimately selected 18 features with the highest predictive value for modeling.

Crucially, the researchers did not commit to a single algorithm. They built and systematically compared 10 machine learning models, including logistic regression, support vector machine, K-nearest neighbors, random forest, ExtraTrees, XGBoost, LightGBM, gradient boosting, AdaBoost, and multilayer perceptron, tuning each via grid search and comparing them with ROC analysis and the DeLong test. The ExtraTrees model emerged as the best radiomics performer, achieving an AUC of 0.653 and accuracy of 0.640 on the validation set, a deliberately conservative benchmark compared with many single-center studies that report higher numbers without independent external testing.

In parallel, multivariate logistic regression identified three independent clinical risk factors for LVI: sentinel lymph node status, estrogen receptor status, and the time-intensity curve pattern of contrast enhancement. The team then integrated the ExtraTrees radiomics score with these clinical predictors to construct a combined model, visualized as a practical nomogram for individualized bedside estimation of LVI probability. The combined model outperformed either the radiomics or clinical model alone across all cohorts, reaching an AUC of 0.796 on the training set, 0.704 on internal validation, and 0.703 on the external test set, with calibration curves and decision curve analysis confirming good agreement and clinical net benefit across a broad range of threshold probabilities.

The authors argue that their relatively modest external AUC values are not a weakness but a feature of honest validation. Many published radiomics studies reporting AUCs above 0.80 relied on single-center data, lenient validation schemes, or highly homogeneous patient subsets. By enrolling all molecular subtypes, including luminal A, luminal B, HER2-overexpressing, and triple-negative cancers, and by testing on a genuinely independent center with real-world data heterogeneity, the study offers a more realistic assessment of how such models behave outside their birthplace. The researchers also note that no dedicated image harmonization strategy, such as ComBat, was applied before feature extraction, which may partly explain the performance drop on external data and marks a target for future improvement.

Limitations remain. The retrospective design carries inherent selection bias, the external cohort was relatively small, and tumor delineation still depends on manual contouring by experienced radiologists, a process that is time-consuming and subject to inter-observer variability. The two centers also used the same scanner vendor, and prospective validation in larger, more diverse populations will be essential before routine clinical deployment. The team outlines next steps that include incorporating diffusion-weighted MRI parameters, applying explainable AI to reveal the imaging basis of model decisions, and integrating the tool into clinical decision support systems to measure its actual impact on surgical planning and patient outcomes.

Even with those caveats, the study delivers a clinically meaningful proof of concept: a noninvasive, preoperative estimate of lymphovascular invasion is achievable when carefully selected radiomic features from DCE-MRI are combined with independent clinical risk factors and validated the hard way. For patients, that could mean smarter surgical decisions and more personalized adjuvant therapy planned before the operation rather than pieced together after it. For the growing field of radiomics, it is a reminder that multicenter external validation, systematic algorithm comparison, and honest reporting of performance are what separate a promising research model from one ready for the clinic.

Subject of Research: Preoperative prediction of lymphovascular invasion in breast cancer using DCE-MRI radiomics and machine learning

Article Title: Machine Learning Comparison and Combined Model Optimization Based on DCE‐MRI Radiomics for Preoperative Assessment of Lymphovascular Invasion in Breast Cancer: A Multicenter Study

Article References: Li, H., Xiao, Q., Zeng, Y., Wang, X., & Zhang, Y. (2026). Machine Learning Comparison and Combined Model Optimization Based on DCE ‐ MRI Radiomics for Preoperative Assessment of Lymphovascular Invasion in Breast Cancer: A Multicenter Study. Cancer Reports, 9(9), Article e70678. https://doi.org/10.1002/cnr2.70678

Image Credits: AI Generated

DOI: 10.1002/cnr2.70678

Keywords: breast cancer, lymphovascular invasion, DCE-MRI, radiomics, machine learning, ExtraTrees, nomogram, multicenter study, external validation, clinical risk factors, LASSO regression, preoperative assessment