Abstract PS3-26: Characterizing errors in perfusion model parameters derived from retrospectively abbreviated quantitative DCE-MRI data
Authors: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov
Journal: Cancer Research (2021)

Abstract Introduction. X-ray mammography is the standard-of-care screening protocol for breast cancer due to its low cost, widespread availability, and greater specificity. While magnetic resonance imaging (MRI) has lower specificity, it has superior tissue contrast, and dynamic contrast-enhanced (DCE) MRI has been shown to increase MRI specificity due to its ability to estimate tissue vascular properties. Because of the increased expense and scan time for MRI, there is an ongoing effort to develop abbreviated breast MRI scans for screening high-risk patients at a lower cost without compromising quantitative information. Here we investigate the effects of the limited dynamic time course afforded by an abbreviated breast MRI exam on the quantitative tissue information computed from retrospectively abbreviated DCE-MRI data. Methods. Data acquisition. We evaluate the error in perfusion model parameters computed using DCE-MRI time courses sourced from three datasets acquired with very different temporal resolutions (dt). These datasets are the ACRIN 6883 multi-site breast trial (dt = 15 s), The University of Texas at Austin (UTA) neoadjuvant therapy study (dt = 7.3 s), and The University of Chicago (UC) ultra-fast breast DCE-MRI study (dt = 3.4 s). Ten representative patients are chosen from each of these datasets for a total of 30 DCE-MRI patient datasets. All 30 full-time courses (FTCs) are retrospectively truncated into a series of abbreviated-time courses (ATCs). An ATC containing the first n post-contrast injection time points of a DCE-MRI time course is referred to as “ATC n.” For the ACRIN dataset, n is the inclusive set of integers from 7 to 18; and, similarly, for the UC data, n is the inclusive set of integers from 12 to 23. For the UTA dataset, n is the inclusive set ranging from 13 through 53, incrementing by eight. Data Analysis. The groups of FTCs and ATCs are analyzed by one of three models, determined by the specifics of the acquisition details of each dataset. The standard Kety-Tofts (SKT) model is fit to the UTA time courses, the reference-region (RR) model is fit to the ACRIN time courses, and the Patlak model is fit to the UC time courses. The volume transfer constant (Ktrans) characterizes tissue enhancement in all three models; whereas, the extravascular/extracellular volume fraction (ve) is specific to the SKT and RR models, and the plasma volume fraction (vp) is specific to the Patlak model. Due to the absence of an arterial input function (AIF) for the ACRIN dataset, the RR model was most appropriate with the pectoral muscle serving as the reference region. The UTA dataset has a population AIF and is thus able to be modeled by the SKT. Lastly, the UC dataset does not capture the tissue washout necessary for ve estimation, so the Patlak model was chosen for analyzing tissue enhancement. Results and Conclusion. The longest ATCs of 4.5, 6.4, and 1.3 min. yielded average errors of 9.1%, 7%, and 3.6% in Ktrans for the ACRIN, UTA, and UC datasets, respectively; and the shortest ATCs of 2, 1.6, and 0.6 min. yielded higher average errors of 24.2%, 22.8%, and 65% in Ktrans, respectively. This is expected from simulations as even the most aggressive ATCs did not substantially exclude the tissue enhancement, characterized by Ktrans. Errors in ve were higher overall since shorter ATCs exclude much of the washout phase. As Ktrans has been shown to discriminate between malignant and benign lesions in full length DCE-MRI scans, it is promising that tolerable errors are observed in the abbreviated MRI setting. There is potential for implementing quantitative abbreviated DCE-MRI scans in the clinic for enhanced diagnostic specificity while freeing up scan time for additional imaging sequences without interfering with standard-of-care image acquisition. Citation Format: Kalina P Slavkova, Julie C DiCarlo, Anum K Syed, Chengyue Wu, John Virostko, Anna G Sorace, Thomas E Yankeelov. Characterizing errors in perfusion model parameters derived from retrospectively abbreviated quantitative DCE-MRI data [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS3-26.