Quantitative imaging in medicine and surgery · 2026
Development and validation of a logistic regression model based on multiparametric MRI for differentiating triple-negative from non-triple-negative breast cancer.
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Zheng Lei, Huang Delong, Zhang Guowei, Cheng Yan, Zhang Jie, Chen Feng
Аннотация
BACKGROUND: Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with a poor prognosis. The preoperative differentiation of TNBC from non-TNBC remains challenging using conventional imaging. This study aimed to develop and validate a logistic regression model based on multiparametric magnetic resonance imaging (MRI) features for distinguishing TNBC from non-TNBC.
METHODS: A total of 286 female patients (mean age 42.9±5.1 years; range, 31-69 years) with pathologically confirmed breast cancer who underwent dynamic contrast-enhanced MRI (DCE-MRI) between January 2019 and September 2024 were retrospectively included. Based on immunohistochemistry, 92 patients were classified as TNBC and 194 as non-TNBC. Multiparametric MRI features, including tumor size, margin, nipple involvement, ipsilateral axillary lymph node metastasis, and Breast Imaging Reporting and Data System (BI-RADS) category, were evaluated by two radiologists. Univariate analysis was performed using rank sum test or chi-square test. Multivariable logistic regression with backward stepwise selection was used to identify independent predictors and construct the predictive model. Model performance was assessed using receiver operating characteristic (ROC) curve analysis, with area under the ROC curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated. Internal validation was performed using 10-fold cross-validation.
RESULTS: Univariate analysis showed that larger tumor size (long diameter: 20.1±14.3 vs. 17.5±4.7 mm, P<0.001; short diameter: 19.4±12.8 vs. 14.0±9.1 mm, P<0.001), unclear tumor margin (89.1% vs. 66.5%, P<0.001), nipple involvement (32.6% vs. 1.5%, P<0.001), solitary ipsilateral axillary lymph node metastasis (53.3% vs. 14.4%, P<0.001), and higher BI-RADS category (mean, 6.75 vs. 6.087, P<0.001) were significantly associated with TNBC. Multivariable logistic regression identified four independent predictors: unclear tumor margin [odds ratio (OR) =43.649; 95% confidence interval (CI): 5.191-367.001; P=0.001], ipsilateral axillary lymph node metastasis (OR =23.901, 95% CI: 6.743-84.714, P<0.001), nipple involvement (OR =9.911; 95% CI: 2.235-44.444; P=0.007), and BI-RADS category 7 (OR =3.288; 95% CI: 1.538-7.027; P=0.002). The logistic regression model achieved an AUC of 0.907 (95% CI: 0.868-0.946). At the optimal probability threshold of 0.62 (Youden index), the model yielded a sensitivity of 0.960, specificity of 0.750, accuracy of 0.887, PPV of 0.877, and NPV of 0.911. The 10-fold cross-validated AUC was 0.891 (95% CI: 0.851-0.931), supporting internal consistency.
CONCLUSIONS: The logistic regression model incorporating multiparametric MRI features-particularly unclear tumor margin, ipsilateral axillary lymph node metastasis, nipple involvement, and BI-RADS category-demonstrates good predictive performance for distinguishing TNBC from non-TNBC. This model may serve as a useful preoperative tool to assist in clinical decision-making for patients with breast cancer.
METHODS: A total of 286 female patients (mean age 42.9±5.1 years; range, 31-69 years) with pathologically confirmed breast cancer who underwent dynamic contrast-enhanced MRI (DCE-MRI) between January 2019 and September 2024 were retrospectively included. Based on immunohistochemistry, 92 patients were classified as TNBC and 194 as non-TNBC. Multiparametric MRI features, including tumor size, margin, nipple involvement, ipsilateral axillary lymph node metastasis, and Breast Imaging Reporting and Data System (BI-RADS) category, were evaluated by two radiologists. Univariate analysis was performed using rank sum test or chi-square test. Multivariable logistic regression with backward stepwise selection was used to identify independent predictors and construct the predictive model. Model performance was assessed using receiver operating characteristic (ROC) curve analysis, with area under the ROC curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) calculated. Internal validation was performed using 10-fold cross-validation.
RESULTS: Univariate analysis showed that larger tumor size (long diameter: 20.1±14.3 vs. 17.5±4.7 mm, P<0.001; short diameter: 19.4±12.8 vs. 14.0±9.1 mm, P<0.001), unclear tumor margin (89.1% vs. 66.5%, P<0.001), nipple involvement (32.6% vs. 1.5%, P<0.001), solitary ipsilateral axillary lymph node metastasis (53.3% vs. 14.4%, P<0.001), and higher BI-RADS category (mean, 6.75 vs. 6.087, P<0.001) were significantly associated with TNBC. Multivariable logistic regression identified four independent predictors: unclear tumor margin [odds ratio (OR) =43.649; 95% confidence interval (CI): 5.191-367.001; P=0.001], ipsilateral axillary lymph node metastasis (OR =23.901, 95% CI: 6.743-84.714, P<0.001), nipple involvement (OR =9.911; 95% CI: 2.235-44.444; P=0.007), and BI-RADS category 7 (OR =3.288; 95% CI: 1.538-7.027; P=0.002). The logistic regression model achieved an AUC of 0.907 (95% CI: 0.868-0.946). At the optimal probability threshold of 0.62 (Youden index), the model yielded a sensitivity of 0.960, specificity of 0.750, accuracy of 0.887, PPV of 0.877, and NPV of 0.911. The 10-fold cross-validated AUC was 0.891 (95% CI: 0.851-0.931), supporting internal consistency.
CONCLUSIONS: The logistic regression model incorporating multiparametric MRI features-particularly unclear tumor margin, ipsilateral axillary lymph node metastasis, nipple involvement, and BI-RADS category-demonstrates good predictive performance for distinguishing TNBC from non-TNBC. This model may serve as a useful preoperative tool to assist in clinical decision-making for patients with breast cancer.