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Nomogram for Early Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using Dynamic Contrast-enhanced and Diffusion-weighted MRI

  • Rui Zhao
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China
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  • Hong Lu
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China
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  • Yan-Bo Li
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China
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  • Zhen-Zhen Shao
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China
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  • Wen-Juan Ma
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China
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  • Pei-Fang Liu
    Correspondence
    Address correspondence to: P.F.L.
    Affiliations
    Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, P.R. China
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Published:February 13, 2021DOI:https://doi.org/10.1016/j.acra.2021.01.023
      Rationale and Objectives: The study investigated the potential of the combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging in predicting the pathological complete response (pCR) of neoadjuvant chemotherapy (NAC) after two cycles of NAC. Materials and methods: Eighty-seven patients with breast cancer who underwent MR examination before and after two cycles of NAC were enrolled. The patients were randomly assigned to a training cohort and a validation cohort (3:1 ratio). MRI parameters including tumor longest diameter, time-signal intensity curve, early enhanced ratio (E90), maximal enhanced ratio and ADC value were measured, and percentage change in MRI parameters were calculated. Univariate analysis and multivariate logistic regression analysis were used to evaluate independent predictors of pCR in the training cohort. The validation cohort was used to test the prediction model, and the nomogram was created based on the prediction model. Results: This study demonstrated that the ADC value after two cycles of NAC (OR = 1.041, 95% CI (1.002, 1.081); p = 0.037), percentage decrease in E90 (OR = 0.927, 95% CI (0.881, 0.977); p =0.004) and percentage decrease in tumor size (OR = 0.948, 95% CI (0.909, 0.988); p = 0.011) were significantly important for independently predicting pCR. The prediction model yielded AUC of 0.939 and 0.944 in the training cohort and the validation cohort, respectively. Conclusion: The combined use of dynamic contrast-enhanced MRI and diffusion-weighted imaging could accurately predict pCR after two cycles of NAC. The prediction model and the nomogram had strong predictive value to NAC.

      Key Words

      Abbreviations:

      ADC (apparent diffusion coefficient), AUC (area under the receiver operating characteristic curve), CI (confidence intervals), DCE-MRI (dynamic contrast-enhanced MRI), DWI (diffusion-weighted imaging), ER (estrogen receptor), HER2 (human epidermal growth factor receptor 2), NAC (Neoadjuvant chemotherapy), OR (odds ratio), pCR (pathological complete response), PR (progesterone receptor), ROC (receiver operating characteristic curve), ROI (region of interest), TIC (time signal intensity curve)
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