Comparison of region-of-interest-averaged and pixel-averaged analysis of DCE-MRI data based on simulations and pre-clinical experiments

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

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 (CV_WS) for each of the magnetic resonance imaging metrics. Determination of PDX volume via manually drawn regions of interest is highly robust, with ∼1% CV_WS. Determination of T2 is also robust with a ∼3% CV_WS. Measurements of T1 and ADC are less robust with CV_WS values in the 6%–11% range. Preliminary DCE test–retest time-course determinations, as quantified by area under the curve and K^trans from 2-compartment exchange (extended Tofts) modeling, suggest that DCE is the least robust protocol, with ∼30%–40% CV_WS.

A compact solution for estimation of physiological parameters from ultrafast prostate dynamic contrast enhanced MRI

The Tofts pharmacokinetic model requires multiple calculations for analysis of dynamic contrast enhanced (DCE) MRI. In addition, the Tofts model may not be appropriate for the prostate. This can result in error propagation that reduces the accuracy of pharmacokinetic measurements. In this study, we present a compact solution allowing estimation of physiological parameters K ^trans and v _e from ultrafast DCE acquisitions, without fitting DCE-MRI data to the standard Tofts pharmacokinetic model. Since the standard Tofts model can be simplified to the Patlak model at early times when contrast efflux from the extravascular extracellular space back to plasma is negligible, K ^trans can be solved explicitly for a specific time. Further, v _e can be estimated directly from the late steady-state signal using the derivative form of Tofts model. Ultrafast DCE-MRI data were acquired from 18 prostate cancer patients on a Philips Achieva 3T-TX scanner. Regions-of-interest (ROIs) for prostate cancer, normal tissue, gluteal muscle, and iliac artery were manually traced. The contrast media concentration as function of time was calculated over each ROI using gradient echo signal equation with pre-contrast tissue T1 values, and using the 'reference tissue' model with a linear approximation. There was strong correlation (r  =  0.88-0.91, p   <  0.0001) between K ^trans extracted from the Tofts model and K ^trans estimated from the compact solution for prostate cancer and normal tissue. Additionally, there was moderate correlation (r  =  0.65-0.73, p   <  0.0001) between extracted versus estimated v _e. Bland-Altman analysis showed moderate to good agreement between physiological parameters extracted from the Tofts model and those estimated from the compact solution with absolute bias less than 0.20 min^-1 and 0.10 for K ^trans and v _e, respectively. The compact solution may decrease systematic errors and error propagation, and could increase the efficiency of clinical workflow. The compact solution requires high temporal resolution DCE-MRI due to the need to adequately sample the early phase of contrast media uptake.

Can DCE-MRI reduce the number of PI-RADS v.2 false positive findings? Role of quantitative pharmacokinetic parameters in prostate lesions characterization

PURPOSE

To test the potential impact of pharmacokinetic parameters, derived from DCE-MRI analysis, on the diagnostic performance of PI-RADSv.2 classification in prostate lesions characterization.

METHOD

Among patients who underwent multiparametric prostate MRI (mpMRI) (January 2016-March 2018) followed by histological evaluation (targeted biopsies/prostatectomy), 103 men were retrospectively selected. For each patient the index lesion was identified and pharmacokinetic parameters (Ktrans, Kep, Ve, Vp) were assessed. MRI diagnostic performance in the detection of significant tumors [Gleason Score (GS)≥7] was assessed, considering PI-RADS≥3 as positive.

RESULTS

GS ≥ 7 (n = 59) showed higher Ktrans (p < 0.01) and Kep (p = 0.01) compared to GS < 7. At ROC curve analysis, a Ktrans cut-off of 191 × 10^-3/min was identified to predict the presence of GS ≥ 7 (AUC:0.75; sensitivity:95%; specificity:61%). Sensitivity and PPV of mpMRI using PI-RADSv.2 were 98% and 61%. Reclassifying PI-RADS≥3 lesions according to Ktrans cut-off, 22 false positives were shifted to true negatives with 3 false negative findings; PPV raised to 79%. Appling Ktrans cut-off to PI-RADS 3 lesions of peripheral zone (n = 18), 12 true negatives, 4 true positives, 2 false positives were identified.

CONCLUSIONS

Despite its high sensitivity prostate mpMRI generates many false positive cases: Ktrans in addition to PIRADS v.2 seems to improve MRI-PPV and may help in avoiding redundant biopsies.

Glycerol dehydration of native and diabetic animal tissues studied by THz-TDS and NMR methods

The optical clearing method has been widely used for different spectral ranges where it provides tissue transparency. In this work, we observed the enhanced penetration of the terahertz waves inside biological samples (skin, kidney, and cornea) treated with glycerol solutions inducing changes of optical and dielectric properties. It was supported by the observed trend of free-to-bound water ratio measured by the nuclear magnetic resonance (NMR) method. The terahertz clearing efficiency was found to be less for diabetic samples than for normal ones. Results of the numerical simulation proved that pulse deformation is due to bigger penetration depth caused by the reduction of absorption and refraction at optical clearing.