Test–Retest Performance of a 1-Hour Multiparametric MR Image Acquisition Pipeline With Orthotopic Triple-Negative Breast Cancer Patient-Derived Tumor Xenografts

Abstract

Preclinical imaging is critical in the development of translational strategies to detect diseases and monitor response to therapy. The National Cancer Institute Co-Clinical Imaging Resource Program was launched, in part, to develop best practices in preclinical imaging. In this context, the objective of this work was to develop a 1-hour, multiparametric magnetic resonance image-acquisition pipeline with triple-negative breast cancer patient-derived xenografts (PDXs). The 1-hour, image-acquisition pipeline includes T1- and T2-weighted scans, quantitative T1, T2, and apparent diffusion coefficient (ADC) parameter maps, and dynamic contrast-enhanced (DCE) time-course images. Quality-control measures used phantoms. The triple-negative breast cancer PDXs used for this study averaged 174 ± 73 μL in volume, with region of interest–averaged T1, T2, and ADC values of 1.9 ± 0.2 seconds, 62 ± 3 milliseconds, and 0.71 ± 0.06 μm2/ms (mean ± SD), respectively. Specific focus was on assessing the within-subject test–retest coefficient-of-variation (CVWS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CVWS. Determination of T2 is also robust with a ∼3% CVWS. Measurements of T1 and ADC are less robust with CVWS values in the 6%–11% range. Preliminary DCE test–retest time-course determinations, as quantified by area under the curve and Ktrans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%–40% CVWS.

Investigating the contribution of hyaluronan to the breast tumour microenvironment using multiparametric MRI and MR elastography

“…s where m is the mean of all the pre-saline and postsaline MRI, Δ is the difference between the paired repeats, and n is the number of pairs [23].…”

Optimal co-clinical radiomics: Sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging

“…The dependency of radiomic features on tumour volume was characterized using 31 image datasets from 12 PDX at different time points volumes for a total of 31 datasets. Theses scans were acquired on an Agilent 4.7T scanner with in-plane spatial resolution of 0.195 £ 0.195 £ 1 mm 3 , as previously described [24]. Table 1A tabulates descriptive data for clinical and preclinical studies to asses sensitivity to noise and resolution, whereas Fig.…”

Respiratory Motion Mitigation and Repeatability of Two Diffusion-Weighted MRI Methods Applied to a Murine Model of Spontaneous Pancreatic Cancer

“…The repeatability metrics provide variability assessments for each DWI protocol. Such assessments would enable the estimation of the sample size required by each DWI method [ 36 ] and are motivated by increased consensus within the cancer imaging community to report these metrics for quantitative imaging markers [ 14 , 27 , 50 , 51 , 52 ]. Considering the highly motion-susceptible location of the pancreatic tumor in freely breathing mice, our reproducibility metrics of the DW-SE-RAD protocol ( SD ws = 0.12 × 10 −3 mm 2 /s and CV WS = 9%) compare favorably to those reported from tumors located in less motion-susceptible locations—for example, the SD ws of 0.1 × 10 −3 mm 2 /s from breast cancer xenografts grown in the hind flank of mice [ 50 ], SD ws of 0.06 × 10 −3 mm 2 /s, and CV WS of 7% from orthotopically implanted breast tumors in mice with restricted respiration [ 27 ].…”

“…Physical restrictions of respiratory motion, such as casting live mice in alginate-molds [ 26 ] or applying a rigid structure [ 27 ], appear to be effective for suppressing motion artifacts. However, concerns of respiratory distress in compromised mice may limit their application.…”