Differentiating Transition Zone Cancers From Benign Prostatic Hyperplasia by Quantitative Multiparametric Magnetic Resonance Imaging.

OBJECTIVE: The aim of this study was to evaluate the value of quantitative diffusion and perfusion parameters to aid in discriminating between transition zone carcinomas and benign prostatic hyperplasia (BPH). MATERIALS AND METHODS: Twenty-four transition zone cancers and BPH nodules were contoured on T2-weighted magnetic resonance imaging (MRI), apparent diffusion coefficient (ADC) maps, and raw dynamic contrast-enhanced (DCE) MRI. Benign prostatic hyperplasia nodules were then stratified into 2 groups based on the presence or absence of a capsule. Apparent diffusion coefficient values, per-voxel Ktrans, kep, vp, and ve were all compared across all groups. RESULTS: Average ADCs (×10 mm/s) were 1019.22, 1338.11, and 1272.46 for cancer, encapsulated BPH, and nonencapsulated BPH, respectively. Both subgroups of BPH were found to be significantly different than that of cancer (P < 0.05). No individual DCE-MRI parameter was significantly different between cancer and either BPH group. The area under the curve for ADC alone was 0.83, and no individual DCE imaging parameter improved the area under the curve of ADC. CONCLUSIONS: Apparent diffusion coefficient may play a role in distinguishing TZ cancers from non-encapsulated BPH nodules that closely resemble cancer.

Semiquantitative and Quantitative Analyses of Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Thyroid Nodules.

OBJECTIVE: This study was aimed to determine the utility of quantitative dynamic contrast-enhanced magnetic resonance imaging (MRI) in differentiating benign and malignant lesions in patients with known thyroid gland lesions scheduled for resection. METHODS: Patients scheduled for resection of a thyroid mass were prospectively enrolled. Dynamic contrast-enhanced MRI scans of the neck were performed before surgery. After resection, patients were divided into benign and malignant groups. Quantitative and semiquantitative MRI kinetic measurements of benign and malignant lesions were compared and analyzed. RESULTS: Twelve benign and 9 malignant lesions were identified in 19 patients. Mean Ktrans, Ve, and Kep for benign lesions were 1.69 ± 1.59 min, 0.44 ± 0.21 min, and 4.51 ± 2.96 min, respectively; for the malignant lesions, 0.96 ± 0.57 min, 0.45 ± 0.19 min, and 3.57 ± 3.53 min, respectively (P = 0.1886, 0.8036, and 0.3028, respectively). Tpeak, ERmax, slopemax, and iAUGC60 for benign lesions were 7.00 ± 8.09 seconds, 293.27 ± 141.25 seconds, 76.45 ± 65.80 seconds, and 63.46 ± 46.84, respectively; for malignant lesions, 8.11 ± 8.55 seconds, 227.6 ± 113.37 seconds, 81.17 ± 109.71 seconds, and 43.69 ± 26.19, respectively (P = 0.7525, 0.4941, 0.4474, and 0.3028, respectively). CONCLUSIONS: Dynamic contrast-enhanced MRI pattern of kinetics was not significantly different for benign and malignant lesions of the thyroid using quantitative or semiquantitative methods.

Prebiopsy magnetic resonance imaging and prostate cancer detection: comparison of random and targeted biopsies.

PURPOSE: We compared the accuracy of visual targeted biopsies vs computerized transrectal ultrasound-magnetic resonance imaging registration using a rigid (Esaote®, nondeformable) or elastic (Koelis®, deformable) approach. MATERIALS AND METHODS: A total of 391 consecutive patients with suspected localized prostate cancer were prospectively included in analysis. All patients underwent prostate magnetic resonance imaging, followed by 10 to 12-core random prostate biopsies. When magnetic resonance imaging detected suspicious findings, targeted biopsy was performed, including visual, rigid system and elastic system targeted biopsies in the first 127 patients, the next 131 and the last 133, respectively. Cancer detection rates were assessed by conditional logistic regression. Targeted biopsies alone and random biopsies were further compared for the amount of tissue sampled and microfocal cancer detection, the latter defined as a single core with 5 mm or less of Gleason 6 cancer. RESULTS: Patient characteristics and random biopsy detection rates were similar among the groups. Magnetic resonance imaging detected at least 1 suspicious area in 54 (42%), 78 (59%) and 82 patients (62%) in groups 1, 2 and 3, respectively. The cancer detection rates of rigid and elastic system targeted biopsies were significantly higher than the random biopsy rate (p = 0.0065 and 0.0016, respectively). Visual targeted biopsy did not perform better than random biopsy (p = 0.66). Rigid and elastic system targeted biopsies allowed for decreasing the number of cores and the detection of microfocal cancer, while increasing the detection of high grade cancer. CONCLUSIONS: When performed with computerized magnetic resonance imaging-transrectal ultrasound image registration, targeted biopsy alone improved cancer detection over random biopsies, decreased the detection rate of microfocal cancer and increased the detection rate of cancer with a Gleason score of greater than 6.

Phase I trial and pharmacokinetic study of sorafenib in children with neurofibromatosis type I and plexiform neurofibromas.

BACKGROUND: Sorafenib targets multiple pathways thought to be crucial in growth of plexiform neurofibroma (PN) in children with neurofibromatosis type 1 (NF1). Sorafenib has been tolerated with manageable toxicities in adults and children with refractory cancer. We conducted a separate study in this population. Monitoring long-term toxicities such as effects on growth and obtaining additional pharmacokinetic data were of importance due to the young age and long duration of therapy seen in previous phase I trials in children with NF1. PROCEDURE: Children ≥3 and ≤18-year-old with NF1 and inoperable PN were eligible. Sorafenib was administered orally twice daily for consecutive 28-day cycles. Maximum tolerated dose (MTD) was determined from toxicities observed during the first three cycles. RESULTS: Nine children enrolled, median age 8 (6-12) years. At the starting 115 mg/m(2) /dose (n = 5), two experienced dose-limiting grade 3 pain in their PN. At the de-escalated 80 mg/m(2) /dose (n = 4), approximately 40% of the pediatric solid tumor MTD, two had dose-limiting toxicity (grade 3 rash and grade 4 mood alteration), exceeding the MTD. At 80 mg/m(2) /dose, the median AUC(0-12 hours) at steady-state was 39.5 µg hours/ml. Toxicities appeared to correspond with decreases in quality of life (QOL). No tumor shrinkage was observed. CONCLUSIONS: Children with NF1 and PN did not tolerate sorafenib at doses substantially lower than the MTD in children and adults with malignant solid tumors. Future trials with targeted agents for children with NF1 may require a more conservative starting dose and separate definitions of dose limiting toxicities (DLT) than children with cancer.

Fully automated prostate segmentation on MRI: comparison with manual segmentation methods and specimen volumes.

OBJECTIVE: The objective of our study was to compare calculated prostate volumes derived from tridimensional MR measurements (ellipsoid formula), manual segmentation, and a fully automated segmentation system as validated by actual prostatectomy specimens. MATERIALS AND METHODS: Ninety-eight consecutive patients (median age, 60.6 years; median prostate-specific antigen [PSA] value, 6.85 ng/mL) underwent triplane T2-weighted MRI on a 3-T magnet with an endorectal coil while undergoing diagnostic workup for prostate cancer. Prostate volume estimates were determined using the formula for ellipsoid volume based on tridimensional measurements, manual segmentation of triplane MRI, and automated segmentation based on normalized gradient fields cross-correlation and graph-search refinement. Estimates of prostate volume based on ellipsoid volume, manual segmentation, and automated segmentation were compared with prostatectomy specimen volumes. Prostate volume estimates were compared using the Pearson correlation coefficient and linear regression analysis. The Dice similarity coefficient was used to quantify spatial agreement between manual segmentation and automated segmentation. RESULTS: The Pearson correlation coefficient revealed strong positive correlation between prostatectomy specimen volume and prostate volume estimates derived from manual segmentation (R = 0.89-0.91, p < 0.0001) and automated segmentation (R = 0.88-0.91, p < 0.0001). No difference was observed between manual segmentation and automated segmentation. Mean partial and full Dice similarity coefficients of 0.92 and 0.89, respectively, were achieved for axial automated segmentation. CONCLUSION: Prostate volume estimates obtained with a fully automated 3D segmentation tool based on normalized gradient fields cross-correlation and graph-search refinement can yield highly accurate prostate volume estimates in a clinically relevant time of 10 seconds. This tool will assist in developing a broad range of applications including routine prostate volume estimations, image registration, biopsy guidance, and decision support systems.

Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management.

OBJECTIVE: This article is a primer on the technical aspects of performing a high-quality dynamic contrast-enhanced MRI (DCE-MRI) examination of the prostate gland. CONCLUSION: DCE-MRI is emerging as a useful clinical technique as part of a multi-parametric approach for evaluating the extent of primary and recurrent prostate cancer. Performing a high-quality DCE-MRI examination requires a good understanding of the technical aspects and limitations of image acquisition and postprocessing techniques.

Socio-Economic Burden of Myasthenia Gravis: A Cost-of-Illness Study in Bulgaria.

Background: Myasthenia gravis (MG) is a chronic autoimmune disorder, which is characterized by fatigable muscle weakness with frequent ocular signs and/or generalized muscle fatigue, and occasionally associated with thymoma. MG patients and their families face a significant socio-economic burden. This population is often experiencing unemployment, unwilling job transfers and decreased income. Objective: This study aimed to estimate the annual costs from a societal perspective in a triple dimension of direct health care costs, direct non-health care costs (formal and informal care) and labor productivity losses in MG patients from Bulgaria, as well as to identify the main clinical and demographical cost drivers. Methods: A bottom-up, cross-sectional, cost-of-illness analysis of 54 adult MG patients was carried out in 2020. To collect data on demographic characteristics, health resource utilization, informal care and productivity losses, questionnaires were administered to and completed by patients. Results and Conclusion: Median annual costs of MG in Bulgaria were 4,047 EUR per patient. Direct costs slightly outweighed indirect costs, with drugs cost item having the biggest monetary impact. Despite the zero-inflated median, hospitalizations also influenced the direct costs by an estimated amount of 1,512 EUR in the 3rd quartile. Social services and professional caregiver costs were found to be almost missing, with the vast majority of patients reporting reliance on informal caregivers. Severe generalized disease, disease crises, and recurrent infections were confirmed as statistically significant cost driving factors. There were no severe generalized MG patients in the bottom quartile of the total costs distribution. It should be noted that in both cases of crises or infections, the overall increase in the total costs was mainly due to higher indirect costs observed. Reliance on family members as informal caregivers is routine among Bulgarian MG patients. This phenomenon is likely due to the lack of access to appropriate social services. Moreover, it is directly related with higher disease burden and significant inequalities. There is a need for further research on MG in Bulgaria in order to design targeted health policies that meet the needs and expectations of these patients.

Dynamic gadolinium-enhanced perfusion MRI of prostate cancer: assessment of response to hypofractionated robotic stereotactic body radiation therapy.

OBJECTIVE: The purpose of this study was to evaluate the utility of dynamic gadolinium-enhanced perfusion MRI for monitoring the response to robotic stereotactic body radiation therapy for prostate cancer. MATERIALS AND METHODS: Eighty-seven patients with prostate cancer underwent dynamic gadolinium-enhanced MRI before robotic stereotactic body radiation therapy, and prostate volume was calculated. Pharmacokinetic analysis postprocessing software was used to generate colorized parametric maps showing perfusion of enhancing tumors. The transfer constant K(trans) was calculated for identified tumors. Follow-up MRI was performed 2 months after treatment for 22 patients, 6 months for 71 patients, 12 months for 54 patients, and 24 months for 27 patients with repeated measurements of prostate volume and K(trans). RESULTS: Perfusion MRI depicted focal enhancing prostate tumors that correlated with the biopsy results in 82 of 87 patients (94%). The median K(trans) of tumors before robotic stereotactic body radiation therapy was 1.79 minutes(-1). Follow-up MRI showed decreases in the size and degree of enhancement of tumors. The median tumor K(trans) decreased to 1.21 minutes(-1) 2 months, 0.39 minutes(-1) 6 months, 0.30 minutes(-1) 12 months, and 0.22 minutes(-1) 24 months after treatment. Prostate volume had decreased 23% 2 months, 26% 6 months, 33% 12 months, and 37% 24 months after robotic stereotactic body radiation therapy. The corresponding median prostate-specific antigen concentration before treatment was 6.45 ng/mL. After treatment, the concentration was 2.90 ng/mL at 2 months, 1.30 ng/mL at 6 months, 1.10 ng/mL at 12 months, and 0.59 ng/mL at 24 months. CONCLUSION: Dynamic gadolinium-enhanced MRI is a useful tool for monitoring the response of prostate cancer to robotic stereotactic body radiation therapy, yielding both qualitative and quantitative data.

Dynamic contrast enhanced-MRI in head and neck cancer patients: variability of the precontrast longitudinal relaxation time (T10).

PURPOSE: Calculation of the precontrast longitudinal relaxation times (T10) is an integral part of the Tofts-based pharmacokinetic (PK) analysis of dynamic contrast enhanced-magnetic resonance images. The purpose of this study was to investigate the interpatient and over time variability of T10 in head and neck primary tumors and involved nodes and to determine the median T10 for primary and nodes (T10(p,n)). The authors also looked at the implication of using voxel-based T10 values versus region of interest (ROI)-based T10 on the calculated values for vascular permeability (K(trans)) and extracellular volume fraction (v(e)). METHODS: Twenty head and neck cancer patients receiving concurrent chemoradiation and molecularly targeted agents on a prospective trial comprised the study population. Voxel-based T10's were generated using a gradient echo sequence on a 1.5 T MR scanner using the variable flip angle method with two flip angles [J. A. Brookes et al., "Measurement of spin-lattice relaxation times with FLASH for dynamic MRI of the breast," Br. J. Radiol. 69, 206-214 (1996)]. The voxel-based T10, K(trans), and v(e) were calculated using iCAD's (Nashua, NH) software. The mean T10's in muscle and fat ROIs were calculated (T10(m,f)). To assess reliability of ROI drawing, T10(p,n) values from ROIs delineated by 2 users (A and B) were calculated as the average of the T10's for 14 patients. For a subset of three patients, the T10 variability from baseline to end of treatment was also investigated. The K(trans) and v(e) from primary and node ROIs were calculated using voxel-based T10 values and T10(p,n) and differences reported. RESULTS: The calculated T10 values for fat and muscle are within the range of values reported in literature for 1.5 T, i.e., T10(m) = 0.958 s and T10(f) = 0.303 s. The average over 14 patients of the T10's based on drawings by users A and B were T10(pA) = 0.804 s, T10(nA) = 0.760 s, T10(pB) = 0.849 s, and T10(nB) = 0.810 s. The absolute percentage difference between K(trans) and v(e) calculated with voxel-based T10 versus T10(p,n) ranged from 6% to 81% and from 2% to 24%, respectively. CONCLUSIONS: There is a certain amount of variability in the median T10 values between patients, but the differences are not significant. There were also no statistically significant differences between the T10 values for primary and nodes at baseline and the subsequent time points (p = 0.94 Friedman test). Voxel-based T10 calculations are essential when quantitative Tofts-based PK analysis in heterogeneous tumors is needed. In the absence of T10 mapping capability, when a relative, qualitative analysis is deemed sufficient, a value of T10(p,n) = 0.800 s can be used as an estimate for T10 for both the primary tumor and the affected nodes in head and neck cancers at all the time points considered.

Tumor Vascularity in Renal Masses: Correlation of Arterial Spin-Labeled and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Assessments.

UNLABELLED: Arterial spin-labeled (ASL) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have been proposed to quantitatively assess vascularity in renal cell carcinoma (RCC). However, there are intrinsic differences between these 2 imaging methods, such as the relative contribution of vascular permeability and blood flow to signal intensity for DCE MRI. We found a correlation between ASL perfusion and the DCE-derived volume transfer constant and rate constant parameters in renal masses > 2 cm in size and these measures correlated with microvessel density in clear cell RCC. BACKGROUND: The objective of this study was to investigate potential correlations between perfusion using arterial spin-labeled (ASL) magnetic resonance imaging (MRI) and dynamic contrast-enhanced (DCE) MRI-derived quantitative measures of vascularity in renal masses > 2 cm and to correlate these with microvessel density (MVD) in clear cell renal cell carcinoma (ccRCC). PATIENTS AND METHODS: Informed written consent was obtained from all patients before imaging in this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, prospective study. Thirty-six consecutive patients scheduled for surgery of a known renal mass > 2 cm underwent 3T ASL and DCE MRI. ASL perfusion measures (PASL) of mean, peak, and low perfusion areas within the mass were correlated to DCE-derived volume transfer constant (K(trans)), rate constant (Kep), and fractional volume of the extravascular extracellular space (Ve) in the same locations using a region of interest analysis. MRI data were correlated to MVD measures in the same tumor regions in ccRCC. Spearman correlation was used to evaluate the correlation between PASL and DCE-derived measurements, and MVD. P < .05 was considered statistically significant. RESULTS: Histopathologic diagnosis was obtained in 36 patients (25 men; mean age 58 ± 12 years). PASL correlated with K(trans) (ρ = 0.48 and P = .0091 for the entire tumor and ρ = 0.43 and P = .03 for the high flow area, respectively) and Kep (ρ = 0.46 and P = .01 for the entire tumor and ρ = 0.52 and P = .008 for the high flow area, respectively). PASL (ρ = 0.66; P = .0002), K(trans) (ρ = 0.61; P = .001), and Kep (ρ = 0.64; P = .0006) also correlated with MVD in high and low perfusion areas in ccRCC. CONCLUSION: PASL correlated with the DCE-derived measures of vascular permeability and flow, K(trans) and Kep, in renal masses > 2 cm in size. Both measures correlated to MVD in clear cell histology.

Comprehensive population-averaged arterial input function for dynamic contrast-enhanced vmagnetic resonance imaging of head and neck cancer.

PURPOSE: To generate a population-averaged arterial input function (PA-AIF) for quantitative analysis of dynamic contrast-enhanced MRI data in head and neck cancer patients. METHODS AND MATERIALS: Twenty patients underwent dynamic contrast-enhanced MRI during concurrent chemoradiation therapy. Imaging consisted of 2 baseline scans 1 week apart (B1/B2) and 1 scan after 1 week of chemoradiation therapy (Wk1). Regions of interest (ROIs) in the right and left carotid arteries were drawn on coronal images. Plasma concentration curves of all ROIs were averaged and fit to a biexponential decay function to obtain the final PA-AIF (AvgAll). Right-sided and left-sided ROI plasma concentration curves were averaged separately to obtain side-specific AIFs (AvgRight/AvgLeft). Regions of interest were divided by time point to obtain time-point-specific AIFs (AvgB1/AvgB2/AvgWk1). The vascular transfer constant (Ktrans) and the fractional extravascular, extracellular space volume (Ve) for primaries and nodes were calculated using the AvgAll AIF, the appropriate side-specific AIF, and the appropriate time-point-specific AIF. Median Ktrans and Ve values derived from AvgAll were compared with those obtained from the side-specific and time-point-specific AIFs. The effect of using individual AIFs was also investigated. RESULTS: The plasma parameters for AvgAll were a1,2 = 27.11/17.65 kg/L, m1,2 = 11.75/0.21 min(-1). The coefficients of repeatability (CRs) for AvgAll versus AvgLeft were 0.04 min(-1) for Ktrans and 0.02 for Ve. For AvgAll versus AvgRight, the CRs were 0.08 min(-1) for Ktrans and 0.02 for Ve. When AvgAll was compared with AvgB1/AvgB2/AvgWk1, the CRs were slightly higher: 0.32/0.19/0.78 min(-1), respectively, for Ktrans; and 0.07/0.08/0.09 for Ve. Use of a PA-AIF was not significantly different from use of individual AIFs. CONCLUSION: A PA-AIF for head and neck cancer was generated that accounts for differences in right carotid artery versus left carotid artery, day-to-day fluctuations, and early treatment-induced changes. The small CRs obtained for Ktrans and Ve indicate that side-specific AIFs are not necessary. However, a time-point-specific AIF may improve pharmacokinetic accuracy.

Dynamic contrast-enhanced MRI in head-and-neck cancer: the impact of region of interest selection on the intra- and interpatient variability of pharmacokinetic parameters.

PURPOSE: Dynamic contrast-enhanced (DCE) MRI-extracted parameters measure tumor microvascular physiology and are usually calculated from an intratumor region of interest (ROI). Optimal ROI delineation is not established. The valid clinical use of DCE-MRI requires that the variation for any given parameter measured within a tumor be less than that observed between tumors in different patients. This work evaluates the impact of tumor ROI selection on the assessment of intra- and interpatient variability. METHOD AND MATERIALS: Head and neck cancer patients received initial targeted therapy (TT) treatment with erlotinib and/or bevacizumab, followed by radiotherapy and concurrent cisplatin with synchronous TT. DCE-MRI data from Baseline and the end of the TT regimen (Lead-In) were analyzed to generate the vascular transfer function (K(trans)), the extracellular volume fraction (v(e)), and the initial area under the concentration time curve (iAUC(1 min)). Four ROI sampling strategies were used: whole tumor or lymph node (Whole), the slice containing the most enhancing voxels (SliceMax), three slices centered in SliceMax (Partial), and the 5% most enhancing contiguous voxels within SliceMax (95Max). The average coefficient of variation (aCV) was calculated to establish intrapatient variability among ROI sets and interpatient variability for each ROI type. The average ratio between each intrapatient CV and the interpatient CV was calculated (aRCV). RESULTS: Baseline primary/nodes aRCVs for different ROIs not including 95Max were, for all three MR parameters, in the range of 0.14-0.24, with Lead-In values between 0.09 and 0.2, meaning a low intrapatient vs. interpatient variation. For 95Max, intrapatient CVs approximated interpatient CVs, meaning similar data dispersion and higher aRCVs (0.6-1.27 for baseline) and 0.54-0.95 for Lead-In. CONCLUSION: Distinction between different patient's primary tumors and/or nodes cannot be made using 95Max ROIs. The other three strategies are viable and equivalent for using DCE-MRI to measure head and neck cancer physiology.