Diffusion weighted imaging in early prediction of neoadjuvant chemotherapy response in breast cancer

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

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.

Diffusion-weighted imaging or MR spectroscopy: Which to use for the assessment of the response to chemotherapy in breast cancer patients?

Background

Diffusion-weighted MRI (DWI) and MR spectroscopy (MRS) both are noninvasive MR sequences that could be used as a reliable tool to assess the functional behavior of the breast cancer. The aim of the study was to assess the value of DWI and MRS in predicting the early response to neo-adjuvant chemotherapy (NAC) and absence of residual disease after treatment.

Results

One hundred thirty-three patients diagnosed with breast cancer and scheduled for NAC were enrolled in this study. All lesions were subjected to qualitative and quantitative analysis of DCE-MRI, DWI and MRS, where the lesions size, kinetic parameters, ADC values and MRS choline peak were recorded before the start of NAC and after completion of chemotherapy. The results of each MRI modality were correlated with the findings that were found at the pathology report of the complete surgical specimen. The sensitivity and specificity of the MR modalities to predict pathological complete remission post-NAC were 73.68% and 83.33%, respectively, using the kinetic curve pattern, 78.95% and 83.33%, respectively, using the ADC value and finally 78.95% and 91.67%, respectively, using the MRS choline peak. Similar sensitivity (89.47%) to predict pathological complete remission was presented by the ADC value and the MRS choline peak together when compared to the ADC value and dynamic curve patterns.

Conclusion

DWI and MRS are valuable MRI techniques and their accuracy in detecting residual disease is almost similar to that of DCE MRI. The inclusion of these sequences in the imaging protocol of NAC candidates improve monitoring of the response to treatment and allow early distinction between complete, partial and non-responders' cases in breast cancer patients.

Quantitative mathematical objective evaluation of contrast-enhanced spectral mammogram in the assessment of response to neoadjuvant chemotherapy and prediction of residual disease in breast cancer

Background

The aim of the study is to initiate a new quantitative mathematical objective tool for evaluation of response to neoadjuvant chemotherapy (NAC) and prediction of residual disease in breast cancer using contrast-enhanced spectral mammography (CESM). Forty-two breast cancer patients scheduled for receiving NAC were included. All patients underwent two CESM examinations: pre and post NAC. To assess the response to neoadjuvant chemotherapy, we used a mathematical image analysis software that can calculate the difference in the intensity of enhancement between the pre and post neoadjuvant contrast images (MATLAB and Simulink) (Release 2013b). The proposed technique used the pre and post neoadjuvant contrast images as inputs. The technique consists of three main steps: (1) preprocessing, (2) extracting the region of interest (ROI), and (3) assessment of the response to chemotherapy by measuring the percentage of change in the intensity of enhancement of malignant lesions in the pre and post neoadjuvant CESM studies using a quantitative mathematical technique. This technique depends on the analysis of number of pixels included within the ROI. We compared this technique with the currently used method of evaluation: RECIST 1.1 (response evaluation criteria in solid tumors 1.1) and using another combined response evaluation approach using both RECIST 1.1 in addition to a subjective visual evaluation. Results were then correlated with the postoperative pathology evaluation using Miller–Payne grades. For statistical evaluation, patients were classified into responders and non-responders in all evaluation methods.

Results

According to the Miller–Payne criteria, 39/42 (92.9%) of the participants were responders (Miller–Payne grades III, IV, and IV) and 3/42 (7.1%) were non-responders (Miller–Payne grades I and II). Using the proposed technique, 39/39 (100%) were responders in comparison to 38/39 patients (97.4%) using the combined criteria and 34/39 (87.2%) using the RECIST 1.1 evaluation. The calculated correlation coefficient of the proposed quantitative objective mathematical technique, RECIST 1.1 criteria, and the combined method was 0.89, 0.59, and 0.69 respectively. With classification of patients into responder and non-responders, the objective mathematical evaluation showed higher sensitivity, positive and negative predictive values, and overall accuracy (100%, 97.5%, 100%, and 85.7% respectively) compared to RECIST 1.1 evaluation (87.2%, 97.1%, 28.6%, and 54.8% respectively) and the combined response method (97.4%, 97.4%, 66.7%, and 85.7% respectively).

Conclusion

Quantitative mathematical objective evaluation using CESM images allows objective quantitative and accurate evaluation of the response of breast cancer to chemotherapy and is recommended as an alternative to the subjective techniques as a part of the pre-operative workup.