Predictive value of DCE-MRI for early evaluation of pathological complete response to neoadjuvant chemotherapy in resectable primary breast cancer: A single-center prospective study

Highlights

• The study constructed multi-parameter MRI model for early prediction of pCR.

• The study was prospective, and contained training and validation sample.

• The study identified easily-measured DCE-MRI parameters.

• The study strengthened DCE-MR be taken at early stage of neoadjuvant therapy.

• The study proved the effectiveness of predictive DCE-MRI model for pCR.

Abstract

Objective

This study proposed to establish a predictive model using dynamic enhanced MRI multi-parameters for early predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer.

Methods

In this prospective cohort study, 170 breast cancer patients treated with NAC were enrolled and were randomly grouped into training sample (136 patients) and validation sample (34 patients). DCE-MRI parameters achieved at the end of the first cycle of NAC were screened to establish the predictive model by using multivariate logistic regression model according to pCR status. Receiver operating characteristic curves were conducted to assess the predictive capability. The association between MRI-predicted pCR and actual pCR in survival outcomes was estimated by using the Kaplan–Meier method with log-rank test.

Results

Multivariate analysis showed ΔAreamax and ΔSlopemax were independent predictors for pCR, odds ratio were 0.939 (95%CI, 0.915 to 0.964), and 0.966 (95%CI, 0.947 to 0.986), respectively. A predictive model was established using training sample as “Y = −0.063*ΔAreamax − 0.034*ΔSlopemax”, a cut-off point of 3.0 was determined. The AUC for training and validation sample were 0.931 (95%CI, 0.890–0.971) and 0.971 (95%CI, 0.923–1.000), respectively. MRI-predicted pCR patients showed similar RFS (p = 0.347), DDFS (p = 0.25) and OS (p = 0.423) with pCR patients.

Conclusion

The multi-parameter MRI model can be potentially used for early prediction of pCR status at the end of the first NAC cycle, which might allow timely regimen refinement before definitive surgical treatment.

Introduction

Neoadjuvant chemotherapy (NAC) for breast cancer has been widely promoted in recent years, and NAC has been made a necessary step for operable patients with breast cancer according to NCCN guidelines [1]. NAC can effectively control the potential metastases at the earliest time and reduce the size and stage of tumor, which to the greatest extent enables breast-conserving surgery, and therefore improves the life quality of breast cancer patients [2].

Several studies proved that patients achieving pathological complete response (pCR) after NAC presented significantly better survival outcomes that those not achieving pCR [3], [4], [5], [6]. Thus, pCR is regarded as a surrogate endpoint for improved survival in breast cancer patients with NAC.

Preoperative accurate prediction of pCR status enables timely regimen adjustment, which is especially crucial when a patient is resistant to NAC. The earlier the pCR status could be predicted, the more clinical benefit would be obtained. Presently, MRI is usually applied in preoperative evaluation of therapeutic response to NAC [7], [8], [9]. Researchers considered several MRI parameters, such as size, apparent diffusion coefficient (ADC), transfer constant (Ktrans), rate constant (Kep) and relative blood volume (rBV) for predicting pCR. However, most of present studies were of small sample size, and used complexly calculated MRI parameters.

Thus we proposed this prospective study to investigate the value of dynamic enhanced MRI (DCE-MRI) for predicting pCR at the end of the first NAC cycle, based on the findings to establish a multi-parameter MRI model for early prediction of pCR.