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• Clinical Article •
Value of preoperative prediction of luminal and non-luminal subtypes of invasive breast cancer based on a dual-sequence interpretable machine learning model
ZHANG Tao  ZHOU Peng  WANG Jun  PANG Zhibin  HU Yuntao 

DOI:10.12015/issn.1674-8034.2025.11.017.


[Abstract] Objective To explore the value of a SHapley Additive exPlanations (SHAP) machine learning model based on dynamic contrast-enhanced (DCE) and high-resolution delayed phase images for the preoperative prediction of luminal and non-luminal subtypes of invasive breast cancer.Materials and Methods Clinical, pathological, and imaging data of 182 patients with pathologically confirmed invasive breast carcinoma of no special type were retrospectively collected and divided into a luminal group (121 cases) and a non-luminal group (61 cases) based on pathological results. Using 3D Slicer software, lesion margins were delineated on DCE and high-resolution delayed phase breast MRI images of invasive breast cancer patients, and radiomic features were extracted. Patients were randomly split into training and test sets in a 7∶3 ratio. Univariate t-test or Mann-Whitney U test and least absolute shrinkage and selection operator (LASSO) were used for feature selection. Clinical models, radiomics models, and combined models were built using logistic regression, support vector machine (SVM), and AdaBoost algorithms, respectively. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Model performance comparisons were conducted using DeLong's test. SHAP analysis was used to visualize feature contributions in the models.Results There were statistically significant differences in histological grade and carbohydrate antigen-125 between the two groups, with P < 0.05. After dimensionality reduction, 2 and 4 optimal radiomics features were respectively retained for DCE and high-resolution delayed-phase images. The combined models of logistic, SVM, and AdaBoost based on DCE features, high-resolution delayed-phase features, and clinical features had better performance. The AUCs in the training set were 0.854, 0.853, and 0.962, respectively, with accuracies of 71.8%, 75.1%, and 89.4%, sensitivities of 74.0%, 77.3%, and 85.1%, and specificities of 69.7%, 72.9%, and 93.6%, respectively. The AUCs in the test set were 0.828, 0.836, and 0.802, respectively, with accuracies of 72.5%, 76.3%, and 72.5%, sensitivities of 74.1%, 77.0%, and 71.8%, and specificities of 67.5%, 74.5%, and 73.5%, respectively. The combined models of logistic and AdaBoost had statistically significant differences between the training set and the test set (P = 0.044, P < 0.001). The combined model of SVM had no statistically significant difference between the training set and the test set (P = 0.277). In the test set, the combined model of SVM was superior to the clinical model of SVM, and the difference was statistically significant (P < 0.001).Conclusions Interpretable machine learning models can preoperatively predict luminal and non-luminal subtypes of invasive breast cancer, holding significant clinical value for formulating personalized treatment plans and prognostic assessments for patients.
[Keywords] magnetic resonance imaging；machine learning；SHapley Additive exPlanations；breast cancer；luminal

ZHANG Tao   ZHOU Peng   WANG Jun   PANG Zhibin   HU Yuntao*  

Department of Radiology, Sichuan Cancer Hospital & Institute (Affiliated to University of Electronic Science and Technology of China), Chengdu 610000, China

Corresponding author: HU Y T, E-mail: 15298217550@163.com

Conflicts of interest   None.

Received  2025-08-02
Accepted  2025-11-03
DOI: 10.12015/issn.1674-8034.2025.11.017
DOI:10.12015/issn.1674-8034.2025.11.017.

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