Abstract P3-02-09: Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams
Authors: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov
Journal: Cancer Research (2022)

Abstract Background: Standard of care (SOC) breast MRI exams typically acquire 4-7 frames of dynamic contrast-enhanced MRI (DCE-MRI) for cancer screening and staging. Post-contrast images depict lesion spiculations and boundaries to identify and characterize tumors. Pharmacokinetic (PK) analysis of DCE-MRI involves modeling blood flow to the lesion and surrounding tissue and has shown promise in diagnosis and prediction of therapeutic response. Currently, SOC DCE-MRI requires ~60-90 seconds per volume for images with sufficient quality and spatial resolution. However, PK analysis of DCE-MRI requires faster time course sampling. For this reason, PK modeling is limited to research scans with lower spatial resolution and higher temporal resolution. PK modeling would improve feedback of treatment response, and implementation in the SOC exam would increase imaging trial participation. In this study, we tested the estimation of Ktrans, a mixed perfusion and permeability PK parameter, from three images at optimal time points after contrast agent (CA) injection, and compared it to the Ktrans estimation from analysis of the full-length time course.. Methods: Women (N=23) with newly diagnosed invasive breast cancers who were eligible for neoadjuvant therapy (NAT) were scanned with a research MRI protocol as part of a treatment-monitoring study. Images acquired prior to the start of NAT were used. MRI was performed on 3.0T Siemens Skyra scanners at two sites with bilateral breast coils. The research protocol included ten sagittal slices centered about the primary tumor. The DCE-MRI images came from a fast sequence with 1.3 × 1.3 × 5.0 mm resolution acquired at 7.3 seconds per frame (66 frames total,) with a gadolinium-based CA injected one minute into the scan. A population arterial input function was used to implement a mathematical graph-based search of possible tissue CA concentration curves from the expected range of PK parameters. The search results gave a set of three optimized sub-sampled timepoints, Topt, from the full set of sample times, Tfull, at which to best sample the CA concentration curves to optimally estimate PK values. The imaging data was analyzed to find one parameter map from image times Tfull, and another from the subset of images at times Topt. The difference in Ktrans was computed at each parameter map voxel, and the concordance correlation coefficient (CCC) was computed per patient to determine agreement. The median Ktrans values were also compared for each patient. Results: The graph-based search of CA concentration curves found optimal times Topt of 37, 66, and 153 seconds after injection. The averaged values over all patients for median and maximum Ktrans from the original Tfull image set were 0.07 and 0.5 (min)-1. The average difference in Ktrans values between the Topt and Tfull sets was 0.02 (min)-1. When the median Ktrans values for each patient were compared, the average difference in median Ktrans values was 15% +/- 9%. The concordance correlation coefficients comparing the Topt and Tfull -sampled parameter maps for each patient were 0.89 +/- 0.12, showing high agreement. Discussion: This retrospective analysis suggests that it is possible to estimate PK parameters from a few properly selected post-contrast images inserted into a SOC DCE-MRI exam. The combination of optimal timing with fast acquisition techniques for high-resolution imaging could be used to provide quantitative data while preserving post-contrast images with the necessary spatial resolution for clinical reading. Importantly, the test images were acquired in the community setting with widely available MRI hardware, further indicating the potential for integration with SOC exams. Funding: NCI U24 CA226110, NCI U01 CA174706, NCI U01 CA142565, CPRIT RR160005 Citation Format: Julie C DiCarlo, Angela M Jarrett, Anum S Kazerouni, John Virostko, Anna G Sorace, Kalina P Slavkova, Debra Patt, Boone W Goodgame, Sarah Avery, Thomas E Yankeelov. Three timepoint pharmacokinetic modeling to incorporate within standard of care MRI breast exams [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P3-02-09.