An MRI-based Scoring System for Preoperative Prediction of Axillary Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer: A Multicenter Retrospective Study

Abstract

To develop and validate a simplified scoring system by integrating MRI and clinicopathologic features for preoperative prediction of axillary pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in clinically node-positive breast cancer. A total of 389 patients from three hospitals were retrospectively analyzed. To identify independent predictors for axillary pCR, univariable and multivariable logistic regression analyses were performed on pre- and post-NAC MRI and clinicopathologic features. Then, a simplified scoring system was constructed based on regression coefficients of predictors in the multivariable model, and its predictive performance was assessed with the receiver operating characteristic curve and calibration curve. The added value of the scoring system for reducing false-negative rate (FNR) of the sentinel lymph node biopsy (SLNB) was also evaluated. The simplified scoring system including seven predictors: progesterone receptor-negative (Three points), HER2-positive (Two points), post-NAC clinical T0-1 stage (Two points), pre-NAC higher ADC value of breast tumor (One point), absence of perinodal infiltration at pre-NAC (One point) and post-NAC MRI (Two points), and absence of enhancement in the tumor bed at post-NAC MRI (Two points), showed good calibration and discrimination, with AUCs of 0.835, 0.828 and 0.798 in the training, internal and external validation cohorts, respectively. The axillary pCR rates were increased with the total points of the scoring system, and patients with a score of ≥11 points had a pCR rate of 86%-100%. In test cohorts for simulating clinical application, the diagnostic accuracy for axillary pCR was 80%-90% among four different radiologists. Compared to standalone SLNB, combining the scoring system with SLNB reduced the FNR from 14.5% to 4.8%. The clinicopathologic-image scoring system with good predictive performance for axillary pCR in clinically node-positive breast cancer, may guide axillary management after NAC and improve patient selection for de-escalating axillary surgery to reduce morbidity.