International journal of women's health · 2026
Multiparametric MRI-Based Prediction Model for Sentinel Lymph Node Metastasis in Breast Cancer: Integrating Quantitative Imaging Parameters and Reproductive History.
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Zhang Yao, Wang Zipei, Li Zhichao, Yuan Ziwei, Liu Tao, Hou Haohao, Ye Zhi, Wang Weibing, Zhang Xiulan
Аннотация
BACKGROUND: Sentinel lymph node metastasis (SLNM) status guides axillary staging and treatment planning in breast cancer, but conventional imaging has limited preoperative predictive accuracy.
METHODS: This retrospective study included 125 consecutive women with pathologically confirmed invasive breast cancer who underwent preoperative multiparametric MRI and sentinel lymph node biopsy between January 2022 and December 2024. Candidate predictors were assessed by univariate analysis and selected with least absolute shrinkage and selection operator regression using 5-fold cross-validation. A multivariable logistic regression model was evaluated for discrimination, calibration, and clinical utility, with internal validation by 1000 bootstrap resamples.
RESULTS: SLNM was present in 57 of 125 patients (45.6%). The final model retained ADC, T1 Map_post, T2* Map, and parity. The model showed excellent discrimination (AUC, 0.960; 95% CI, 0.927-0.985), good calibration (Brier score, 0.082), and positive net clinical benefit across threshold probabilities of approximately 10%-80%. Bootstrap validation yielded an optimism-corrected AUC of 0.956.
CONCLUSION: A multiparametric MRI-based model incorporating ADC, T1 Map_post, T2* Map, and parity showed strong performance for preoperative SLNM prediction and may support individualized surgical planning after external validation.
METHODS: This retrospective study included 125 consecutive women with pathologically confirmed invasive breast cancer who underwent preoperative multiparametric MRI and sentinel lymph node biopsy between January 2022 and December 2024. Candidate predictors were assessed by univariate analysis and selected with least absolute shrinkage and selection operator regression using 5-fold cross-validation. A multivariable logistic regression model was evaluated for discrimination, calibration, and clinical utility, with internal validation by 1000 bootstrap resamples.
RESULTS: SLNM was present in 57 of 125 patients (45.6%). The final model retained ADC, T1 Map_post, T2* Map, and parity. The model showed excellent discrimination (AUC, 0.960; 95% CI, 0.927-0.985), good calibration (Brier score, 0.082), and positive net clinical benefit across threshold probabilities of approximately 10%-80%. Bootstrap validation yielded an optimism-corrected AUC of 0.956.
CONCLUSION: A multiparametric MRI-based model incorporating ADC, T1 Map_post, T2* Map, and parity showed strong performance for preoperative SLNM prediction and may support individualized surgical planning after external validation.