Diffusion Weighted and Diffusion Tensor MRI-derived Apparent Diffusion Coefficient and Fractional Anisotropy Values as Biomarkers for Treatment Response in Breast Cancer

Abstract

Background

With increasing numbers of patients receiving neoadjuvant chemotherapy (NACT) for breast cancer and with multiple emerging drug agents, a reliable indicator of treatment response early in the course of chemotherapy is vital to facilitate tailored treatment for individual patients. Determination of response is unreliable with clinical assessment, and conventional imaging techniques such as ultrasonography and mammography are of limited use as they primarily detect changes in tumour size which may not become evident for several weeks after the initiation of chemotherapy. Functional MRI (including diffusion imaging) provides an opportunity to detect alterations in the cellular environment early in the course of chemotherapy. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values are quantitative parameters derived from diffusion MRI.

Purpose

To evaluate if changes in ADC and FA values can predict early response in patients receiving NACT for breast cancer.

Methods

20 consecutive patients with invasive breast carcinoma underwent 3.0 Tesla MRI at 4 time-points: pre-treatment (TP0) and following the first (TP1), second (TP2) and final cycles of NACT. ADC and FA maps were generated for each patient at each time-point. Baseline and sequential data in responder and non-responder groups were compared toassess the potential of ADC and FA in predicting tumour response, as determined by Miller Payne grading of the surgical specimen.

Results

Tumour ADC in responders significantly increased at TP1 (p<0.001) and TP2 (p<0.001) over baseline, while a significant increase in tumour FA of responders was seen at TP2 (p=0.005) only. No statistical change occurred in tumour ADC or FA values in the non-responder group. The percentage increase in mean tumour ADC was significantly higher in the responder group compared to the non-responder group after the first and second cycles of chemotherapy (p<0.001). The optimal time point to discriminate responders from non-responders was after the first cycle of chemotherapy with a percentage increase cut off in tumour ADC values of 7.7%. Despite tumour FA significantly increasing in the responder group from TP0 to TP2, no significant percentage change in FA values was observed between responders and non-responders.

Conclusion

Changes in ADC values early in the course of treatment are useful for predicting response in patients receiving NACT for breast cancer. Although tumour FA significantly increased in the responder group after the second cycle of chemotherapy, FA values did not demonstrate efficacy in the early differentiation of responders from non-responders.