Related Experiment Video
Updated: Aug 24, 2025

Endoscopic Bilateral Nipple-sparing Mastectomy via a Single Axillary Incision with Immediate Pre-pectoral Implant-based Breast Reconstruction
Published on: May 17, 2024
An MRI-based Scoring System for Preoperative Prediction of Axillary Response to Neoadjuvant Chemotherapy in
Xiaomei Huang1, Zhenwei Shi2, Jinhai Mai3
1The Second School of Clinical Medicine, Southern Medical University, Guangzhou, Guangdong, China; Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China; Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China.
Rationale And Objectives:
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.
Materials And Methods:
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.
Results:
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%.
Conclusion:
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.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
15:48Tracking the Mammary Architectural Features and Detecting Breast Cancer with Magnetic Resonance Diffusion Tensor Imaging
Published on: December 15, 2014