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

Authors

Hongen Li, Qingwen Xiao, Yihui Zeng, Xia Wang, Yan Zhang

Published in

Cancer reports (Hoboken, N.J.). Volume 9. Issue 9. Pages e70678.

Abstract

To explore the value of constructing a radiomics combined model based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for predicting lymphovascular invasion (LVI) in breast cancer.
Retrospective data collection was performed from December 2022 to November 2025, involving 912 patients with pathologically confirmed breast cancer who underwent DCE-MRI examinations at two medical centers. Data from 757 patients at Center 1 (Guangdong Provincial Maternal and Child Health Hospital) were used as the model development set. Through stratified random sampling, this set was divided into a training set (n = 529) and an internal validation set (n = 228) at a 7:3 ratio. The 155 patients from Center 2 (The First Affiliated Hospital of Jinan University) formed an independent external test set. Three-dimensional regions of interest (ROIs) were manually delineated on DCE-MRI images, and 1197 radiomics features were extracted using the PyRadiomics software. Within the training set, key features were selected through univariate analysis (p < 0.05) using Pearson correlation analysis (|r| < 0.9) and LASSO regression (10-fold cross-validation). Key features were evaluated using logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extreme trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), gradient boosting machine (GBM), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) to construct radiomics models, identifying the model with the highest predictive performance. Simultaneously, univariate and multivariate logistic regression analyses were employed to identify independent risk factors for breast cancer LVI from clinical and pathological characteristics, and a clinical model was constructed. A combined model was constructed by integrating the independent risk factors for breast cancer LVI with the optimal radiomics model, and a nomogram was developed. The predictive performance and clinical utility of each model for breast cancer LVI were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Based on DCE-MRI images, 18 key features were ultimately selected for constructing the radiomics model. Among these, the ExtraTrees radiomics model demonstrated the highest predictive performance, achieving an area under the curve (AUC) of 0.812 (95% CI: 0.7768-0.8480) on the training set and 0.653 (95% CI: 0.5977-0.7081), with accuracy superior to the other nine machine learning models in the validation set. Independent risk factors for breast cancer LVI included sentinel lymph node status, ER, and TIC, which were used to construct a clinical model. The AUC values for the training set were 0.700 (95% CI: 0.6557-0.7440), 0.752 (95% CI: 0.7101-0.7933), and 0.796 (95% CI: 0.7582-0.8341). The corresponding AUC values on the validation set were 0.642 (95% CI: 0.5695-0.7145), 0.664 (95% CI: 0.5901-0.7374), and 0.704 (95% CI: 0.6339-0.7745). The AUC values for the test set were 0.603 (95% CI: 0.5131-0.6923), 0.692 (95% CI: 0.6097-0.7739), and 0.703 (95% CI: 0.6187-0.7866). Calibration curves demonstrated good agreement between the combined model's predictions and actual observed outcomes. DCA indicated that the model provided greater clinical net benefit across a broad range of threshold probabilities.
The combined model constructed based on DCE-MRI effectively predicts LVI in breast cancer, demonstrating superior performance compared to standalone radiomics or clinical models. It exhibits excellent calibration and clinical utility, offering promising potential to support preoperative individualized treatment decisions.

PMID:
42732916
Bibliographic data and abstract were imported from PubMed on 14 Sep 2026.

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