Symposium



High resolution DWI to evaluate breast tumor treatment response: Association of ADC changes with pCR


Presenting Author Senior Author
Name: Lisa Wilmes Name: Nola Hylton
Email: lisa.wilmes@ucsf.edu Email: nola.hylton@ucsf.edu
Presenting Author’s RIG/SRG: Breast Cancer  
Presenting Author's Lab Location: Mount Zion   

Abstract Information
Imaging Modality: MR
Disease Application: Breast Cancer
Complete author list: Lisa J Wilmes, Wei-Ching Lo, Wen Li, David C Newitt, Suchandrima Banerjee, Evelyn Proctor, Emine U Saritas, Ajit Shankaranarayanan, Nola M Hylton
Abstract highlights: A high spatial-resolution diffusion weighted imaging technique was evaluated in breast cancer patients undergoing neoadjuvant chemotherapy to determine the association between early change in tumor apparent diffusion coefficient (ADC) and pathologic complete response. Higher AUCs were found for early ADC changes than for early tumor volume changes.
 
Introduction
Diffusion-weighted imaging (DWI) provides information about tissue microstructure and has shown promise as a potential biomarker of early treatment response in breast cancer. One limitation of single shot echo-planar imaging (ssEPI) DWI is that the spatial resolution is typically much lower than the standard T1-weighted acquisition used for DCE-MRI. Our group has optimized a high-resolution ssEPI reduced-FOV DWI (HR-DWI) acquisition for breast imaging. The sequence utilizes a 2D spatially-selective echo-planar RF excitation pulse and a 180-degree refocusing pulse to reduce the FOV in the phase-encode direction and has shown improved image quality compared to standard DWI in breast. This work investigated the association between tumor pathologic complete response (pCR), a clinical measure of tumor response, and early changes in tumor ADC metrics measured with HR-DWI.
 
Methods
Twenty patients with invasive breast cancer were scanned with HR-DWI before (pre-treatment) and after one cycle (early-treatment) of neoadjuvant taxane based treatment as part of an ongoing IRB approved study at our institution. All patients gave informed consent. Imaging was performed on a 1.5T GE Signa scanner using an 8 channel bilateral phased array Sentinelle breast coil. HR-DWI acquisition parameters were: TR/TE: 4000ms/64.8ms, FOV: 140x70 mm, matrix: 128x64, NEX: 16, b=0,600 s/mm2, voxel size: 4.8mm3. DCE-MRI data were also acquired for all patients at the pre-treatment and early treatment time points. For DWI data analysis, ADC maps were calculated and one tumor region of interest (ROI) was defined on the HR-DWI slice estimated to contain the largest tumor area. Mean tumor ADC as well as 5th, 15th, 25th, 50th, 75th, and 95th percentile ADCs were calculated and evaluated as predictors of pCR. DCE-MRI measured tumor volume change between the pre-treatment MRI and early treatment MRI was similarly evaluated for comparison. The association between early change predictors (HR-DWI ADC metrics and tumor volume) and pCR was evaluated using receiver operating characteristic analysis to obtain the area under the curve (AUC) for the full cohort of patients, and also for the subset of 14 patients that were both hormone receptor positive (HR+) and human epidermal growth factor receptor 2 negative (HER2-). Other tumor subtypes (eg. triple negative, HR-/HER2-) were not evaluated due to limited sample sizes.
 
Results
Table 1 shows the AUCs for the early HR-DWI ADC predictors and tumor volume change predictor for the full cohort and the HR+/HER2- subset. For early percent change in tumor ADC a trend of increasing AUC with decreasing percentile ADC was observed. Additionally, the AUCs for the lower percentile tumor ADC were higher than for the early tumor volume change. These effects were stronger when only the HR+/HER2- subset was considered.
 
Conclusions
The higher AUCs found for early changes in ADC metrics, in particular lower percentile ADC metrics, versus early tumor volume suggest that HR-DWI may be of value in evaluating early breast tumor response to neoadjuvant chemotherapy. These results also suggest that characterization of early change in breast tumor ADC metrics may be affected by the tumor genetic subtype.