Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

Abstract

Background The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. Methods Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. Results Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. Conclusions Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

Analysis of simplicial complexes to determine when to sample for quantitative DCE MRI of the breast

“…An additional high‐spatial‐resolution (1‐mm 3 isotropic) T 1 ‐weighted volumetric interpolated breath‐hold sequence with spectrally selective adiabatic inversion‐recovery fat suppression was used to acquire 96 slices before and after the DCE‐MRI sequence to provide an anatomical reference for tumor segmentation. Full details of all sequence parameters can be found in Virostko et al 12 …”

“…A variable flip‐angle T 1 ‐mapping sequence was followed by a B 1 ‐mapping sequence 30 acquired over the same FOV. A DCE‐MRI T 1 ‐weighted volumetric interpolated breath‐hold sequence with TR/TE of 7.9/2.4 ms and flip angle of 6° or 15° (the flip angle was modified during the study) was used to acquire ten 5.0‐mm‐thick slices before, during, and after contrast injection 12,13 . The DCE‐MRI volumes were imaged with a spatial resolution of 1.3 × 1.3 × 5.0 mm 3 every 7.3 s for a total of 66 time points (8 min).…”

“…Changes in quantitative perfusion assessments-such as measures of contrast agent kinetics-can characterize tumor physiology. [8][9][10] These have been demonstrated in the community setting [11][12][13] and have been shown to provide additional specificity for both diagnostic [14][15][16][17][18] and prognostic application. 19,20 The challenge in adding quantitative DCE MRI to a SOC exam is that it requires imaging with temporal resolution that is about 10 times faster than SOC scans, during the same 5-7-min postcontrast injection window (Figure 1).…”

“…Changes in quantitative perfusion assessments—such as measures of contrast agent kinetics—can characterize tumor physiology 8–10 . These have been demonstrated in the community setting 11–13 and have been shown to provide additional specificity for both diagnostic 14–18 and prognostic application 19,20 …”

An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data

“…12 T 1 parameter mapping is also an important component in other quantitative schemes, such as pharmacokinetic analysis of dynamic contrast-enhanced (DCE) MRI in breast cancer imaging 13 and brain imaging [12][13][14] for diagnosis 13,15 and treatment monitoring. [16][17][18][19] Despite the utility of quantitative imaging, scan time constraints remain a barrier to incorporating quantitative imaging schemes into standard of care protocols. To combat this issue, many investigators are attempting to reduce scan time necessary for clinical application through model-based, multi-contrast methods.…”