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

  • Xiaomei Huang
    Affiliations
    The 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
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  • Zhenwei Shi
    Affiliations
    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

    Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China
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  • Jinhai Mai
    Affiliations
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
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  • Chunling Liu
    Affiliations
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
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  • Chen Liu
    Affiliations
    The 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
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  • Shuting Chen
    Affiliations
    Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, Guangdong, China
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  • Hong Lu
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
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  • Yanbo Li
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin’s Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, China
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  • Bo He
    Affiliations
    Department of Radiology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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  • Jun Li
    Affiliations
    Department of Radiology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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  • Hanxue Cun
    Affiliations
    Department of Radiology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China
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  • Chu Han
    Affiliations
    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

    Guangdong Cardiovascular Institute, Guangzhou, Guangdong, China
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  • Xin Chen
    Affiliations
    Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, China
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  • Changhong Liang
    Affiliations
    The 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
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  • Zaiyi Liu
    Correspondence
    Address correspondence to: Z. L.
    Affiliations
    The 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
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Published:October 21, 2022DOI:https://doi.org/10.1016/j.acra.2022.09.022

      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.

      Key Words

      Abbreviations:

      ALND (Axillary lymph node dissection), AUC (Area under the receiver operating characteristic curve), CI (Confidence interval), ER (Estrogen receptor), FNR (False-negative rate), HER2 (Human epidermal growth factor receptor 2), NAC (Neoadjuvant chemotherapy), OR (Odds ratio), pCR (Pathologic complete response), PR (Progesterone receptor), SLNB (Sentinel lymph node biopsy)
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