Quantitative MRI Assessment of Breast Cancer Therapy Response

Compare functional MRI biomarkers with tumor size measurement for early prediction of breast cancer response to neoadjuvant chemotherapy

Functional MRI biomarkers, Kᵗʳᵃⁿˢ (transfer rate constant in min-¹), vₑ (extravascular and extracellular space volume fraction), kₑₚ (efflux rate constant in min-¹), and τᵢ (mean intracellular water lifetime in sec) along with ADC (apparent diffusion coefficient in mm²/sec) will be obtained from DCE- and DW-MRI. Tumor longest diameter (LD) will be measured (in mm). Early changes (%) in each MRI parameter will be derived and correlated with pathologic response status, pathologic complete response (pCR) or non-pCR, obtained from the resected tumor. Predictive accuracy for pCR vs. non-pCR is determined by area under the receiver operating characteristic curve (ROC AUC) for each parameter change (%). Comparison of AUC will determine if change (%) of a functional MRI biomarker is more accurate than LD in prediction of pCR vs. non-pCR. Also, changes (%) of all functional biomarkers will be combined using multivariate logistic regression and compared with that of LD for predictive accuracy.

Compare functional MRI biomarkers with tumor size measurement for assessment of residual cancer burden

MRI parameters Kᵗʳᵃⁿˢ, vₑ, kₑₚ, τᵢ, ADC, and LD derived from the MRI data obtained after completion of neoadjuvant chemotherapy but before surgery will be correlated with residual cancer burden (RCB) ranks (RCB I, II, and III) determined by standard of care pathologist review of surgical specimens. Correlation coefficients will be compared to determine if a functional MRI biomarker is more accurate than LD in assessing RCB ranks. In addition, all functional MRI biomarkers will be combined using a multivariate logistic regression model and compared with LD in assessing RCB ranks.

Compare functional MRI biomarkers with tumor size measurement for prognosis of recurrence-free survival

MRI parameters Kᵗʳᵃⁿˢ, vₑ, kₑₚ, τᵢ, ADC, and LD derived from the MRI data obtained after completion of neoadjuvant chemotherapy but before surgery will be correlated with recurrence-free survival (in the unit of month) to determine if a functional MRI biomarker provides more accurate prognosis of five-year recurrence-free survival than LD. In addition, all functional MRI biomarkers will be combined using a multivariate logistic regression model and compared with LD in prognosis of recurrence-free survival.

Validation of Shutter-Speed dynamic contrast-enhanced (DCE) MRI as a robust method for prediction and evaluation of breast cancer response to neoadjuvant chemotherapy

DCE-MRI data will be collected from participants using a Siemens scanner at OHSU, a Philips scanner at University of Washington, and a General Electric (GE) scanner at University of Iowa. The Shutter-Speed pharmacokinetic model will be used to analyze DCE-MRI data from each site to derive Kᵗʳᵃⁿˢ, vₑ, kₑₚ, and τᵢ parameters. As described above, ROC AUC value for prediction of response to neoadjuvant chemotherapy and correlation coefficient with RCB ranks will be calculated for each functional DCE-MRI biomarker. The ROC AUC value and correlation coefficient for each functional DCE-MRI biomarker will be compared across the three scanners (or three sites), respectively, to determine if the functional DCE-MRI biomarkers derived with the Shutter-Speed pharmacokinetic model provide similar predictive performances regardless of the MRI scanner vendor.