The differential proteomic response to ischemic stroke in appalachian subjects treated with mechanical thrombectomy.

INTRODUCTION: The Appalachia region of North America is known to have significant health disparities, specifically, worse risk factors and outcomes for stroke. Appalachians are more likely to have comorbidities related to stroke, such as diabetes, obesity, and tobacco use, and are often less likely to have stroke interventions, such as mechanical thrombectomy (MT), for emergent large vessel occlusion (ELVO). As our Comprehensive Stroke Center directly serves stroke subjects from both Appalachian and non-Appalachian areas, inflammatory proteomic biomarkers were identified associated with stroke outcomes specific to subjects residing in Appalachia. METHODS: There were 81 subjects that met inclusion criteria for this study. These subjects underwent MT for ELVO, and carotid arterial blood samples acquired at time of intervention were sent for proteomic analysis. Samples were processed in accordance with the Blood And Clot Thrombectomy Registry And Collaboration (BACTRAC; clinicaltrials.gov; NCT03153683). Statistical analyses were utilized to examine whether relationships between protein expression and outcomes differed by Appalachian status for functional (NIH Stroke Scale; NIHSS and Modified Rankin Score; mRS), and cognitive outcomes (Montreal Cognitive Assessment; MoCA). RESULTS: No significant differences were found in demographic data or co-morbidities when comparing Appalachian to non-Appalachian subjects. However, time from stroke onset to treatment (last known normal) was significantly longer and edema volume significantly higher in patients from Appalachia. Further, when comparing Appalachian to non-Appalachian subjects, there were significant unadjusted differences in the NIHSS functional outcome. A comprehensive analysis of 184 proteins from Olink proteomic (92 Cardiometabolic and 92 Inflammation panels) showed that the association between protein expression outcomes significantly differed by Appalachian status for seven proteins for the NIHSS, two proteins for the MoCA, and three for the mRS. CONCLUSION: Our study utilizes an ELVO tissue bank and registry to investigate the intracranial/intravascular proteomic environment occurring at the time of thrombectomy. We found that patients presenting from Appalachian areas have different levels of proteomic expression at the time of MT when compared to patients presenting from non-Appalachian areas. These proteins differentially relate to stroke outcome and could be used as prognostic biomarkers, or as targets for novel therapies. The identification of a disparate proteomic response in Appalachian patients provides initial insight to the biological basis for health disparity. Nevertheless, further investigations through community-based studies are imperative to elucidate the underlying causes of this differential response.

Now that the door is open: an update on ischemic stroke pharmacotherapeutics for the neurointerventionalist.

The last 10 years have seen a major shift in management of large vessel ischemic stroke with changes towards ever-expanding use of reperfusion therapies (intravenous thrombolysis and mechanical thrombectomy). These strategies 'open the door' to acute therapeutics for ischemic tissue, and we should investigate novel therapeutic approaches to enhance survival of recently reperfused brain. Key insights into new approaches have been provided through translational research models and preclinical paradigms, and through detailed research on ischemic mechanisms. Additional recent clinical trials offer exciting salvos into this new strategy of pairing reperfusion with neuroprotective therapy. This pairing strategy can be employed using drugs that have shown neuroprotective efficacy; neurointerventionalists can administer these during or immediately after reperfusion therapy. This represents a crucial moment when we emphasize reperfusion, and have the technological capability along with the clinical trial experience to lead the way in multiprong approaches to stroke treatment.

Plasma protein alterations during human large vessel stroke: A controlled comparison study.

BACKGROUND: Stroke is a major cause of death and disability in the United States. Mechanical thrombectomy (MT) and tissue plasminogen activator are the current treatments for ischemic stroke, which have improved clinical outcomes. Despite these treatments, functional and cognitive deficits still occur demonstrating a need for predictive biomarkers for beneficial clinical outcomes which can be used as therapeutic targets for pharmacotherapy. The aim of this study compares the proteomic expression of systemic arterial blood collected at the time of MT to those from a matched cerebrovascular disease (CVD) control cohort. METHODS: The Blood And Clot Thrombectomy Registry And Collaboration (BACTRAC) (clinicaltrials.gov NCT03153683) collects and banks arterial blood, both distal and proximal to the thrombus, from ischemic stroke subjects undergoing MT. Arterial blood from patients undergoing a diagnostic angiogram was also collected and banked as CVD controls. Changes in cardiometabolic and inflammatory proteins between stroke and CVD controls were analyzed via Olink Proteomics. RESULTS: Proteins including ARTN, TWEAK, HGF, CCL28, FGF-5, CXCL9, TRANCE and GDNF were found to be decreased in stroke subjects when compared to CVD controls. CXCL1, CCL5, OSM, GP1BA, IL6, MMP-1, and CXCL5 were increased in stroke subjects when compared to CVD controls. These proteins were also significantly correlated to stroke outcome metrics such as NIHSS, infarct volume and MoCA scoring. CONCLUSION: Overall, acute stroke patients had an increase in inflammatory proteins with a decrease in trophic proteins systemically compared to matched CVD controls. Using our CVD controls, proteins of interest were directly compared to stroke patients with the same cerebrovascular risk factors instead of statistically controlling for comorbidities. The novel methodology of matching an arterial blood CVD control group to a stroke group, as well as controlling for age and comorbid status add to the literature on prognostic stroke biomarkers, which are specific targets for future therapeutics.

Influence of BMI on adenosine deaminase and stroke outcomes in mechanical thrombectomy subjects.

BACKGROUND: Emergent Large Vessel Occlusion (ELVO) strokes are ischemic vascular events for which novel biomarkers and therapies are needed. The purpose of this study is to investigate the role of Body Mass Index (BMI) on protein expression and signaling at the time of ELVO intervention. Additionally, we highlight the protein adenosine deaminase (ADA), which is a deaminating enzyme that degrades adenosine, which has been shown to be neuroprotective in ischemia. We investigate the relationship between ADA and BMI, stroke outcomes, and associated proteomic networks which might aid in personalizing prognosis and future treatment of ELVO stroke. METHODS: The Blood And Clot Thrombectomy And Collaboration (BACTRAC) study is a continually enrolling tissue bank (clinicaltrials.gov NCT03153683) and registry from stroke patients undergoing mechanical thrombectomy (MT). N â€‹= â€‹61 human carotid plasma samples were analyzed for inflammatory and cardiometabolic protein expression by Olink Proteomics. Statistical analyses used t-tests, linear, logistic, and robust regressions, to assess the relationship between BMI, proteomic expression, and stroke-related outcomes. RESULTS: The 61 subjects studied were broken into three categories: normal weight (BMI 18.5-24.9) which contained 19 subjects, overweight (BMI 25-30) which contained 25 subjects, and obese (BMI ≥30) which contained 17 subjects. Normal BMI group was a significantly older population (mean 76 years) when compared to overweight (mean 66 years) and obese (mean 61 years) with significance of p â€‹= â€‹0.041 and p â€‹= â€‹0.005, respectively. When compared to normal weight and overweight categories, the obese category had significantly higher levels of adenosine deaminase (ADA) expression (p â€‹= â€‹0.01 and p â€‹= â€‹0.039, respectively). Elevated levels of ADA were found to have a significant positive correlation with both infarct volume and edema volume (p â€‹= â€‹0.013 and p â€‹= â€‹0.041, respectively), and were associated with a more severe stroke (NIHSS on discharge) and greater stroke related disability (mRS on discharge) with significance of p â€‹= â€‹0.053 and p â€‹= â€‹0.032, respectively. CONCLUSIONS: When examined according to BMI, subjects undergoing MT for ELVO demonstrate significant differences in the expression of certain plasma proteins, including ADA. Levels of ADA were found to be significantly higher in the obese population when compared to normal or overweight groups. Increased levels of ADA in the obese group were predictive of increased infarct volume, edema volume, and worse NIHSS scores and mRS at discharge. These data provide novel biomarker candidates as well as treatment targets while increasing the personalization of stroke prognosis and treatment.

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.

OBJECTIVES: To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ratios (TCRs) on whole mount histopathology of prostate cancer (PCa) and prostatitis. MATERIALS AND METHODS: A retrospective, IRB-approved, HIPAA-compliant cohort consisting of 14 PCa patients who underwent 3 T multiparametric MRI along with T1 and T2 MRF maps prior to radical prostatectomy was used. Correspondences between whole mount specimens and MRI and MRF were manually established. Prostatitis, PCa, and normal peripheral zone (PZ) regions of interest (ROIs) on pathology were segmented for TCRs of epithelium, lumen, and stroma using two U-net deep learning models. Corresponding ROIs were mapped to T2-weighted MRI (T2w), apparent diffusion coefficient (ADC), and T1 and T2 MRF maps. Their correlations with TCRs were computed using Pearson's correlation coefficient (R). Statistically significant differences in means were assessed using one-way ANOVA. RESULTS: Statistically significant differences (p < 0.01) in means of TCRs and T1 and T2 MRF were observed between PCa, prostatitis, and normal PZ. A negative correlation was observed between T1 and T2 MRF and epithelium (R = - 0.38, - 0.44, p < 0.05) of PCa. T1 MRF was correlated in opposite directions with stroma of PCa and prostatitis (R = 0.35, - 0.44, p < 0.05). T2 MRF was positively correlated with lumen of PCa and prostatitis (R = 0.57, 0.46, p < 0.01). Mean T2 MRF showed significant differences (p < 0.01) between PCa and prostatitis across both transition zone (TZ) and PZ, while mean T1 MRF was significant (p = 0.02) in TZ. CONCLUSION: Significant associations between MRF (T1 in the TZ and T2 in the PZ) and tissue compartments on corresponding histopathology were observed. KEY POINTS: • Mean T2 MRF measurements and ADC within cancerous regions of interest dropped with increasing ISUP prognostic groups (IPG). • Mean T1 and T2 MRF measurements were significantly different (p < 0.001) across IPGs, prostatitis, and normal peripheral zone (NPZ). • T2 MRF showed stronger correlations in the peripheral zone, while T1 MRF showed stronger correlations in the transition zone with histopathology for prostate cancer.

Relationship between Mammographic Findings and Breast Abnormalities in a Nigerian Population.

PURPOSE: This study aims to describe the mammographic findings in a population of Nigerian women and to explore the relationships between abnormal mammographic findings, breast malignancy, and breast composition. METHODOLOGY: This was a retrospective study of consecutive mammograms carried out at Union Diagnostics and Clinical Services in Lagos, Nigeria from 2016 to 2018. Demographic information, indications for and findings on mammographic evaluation were obtained. A logistic regression fit model was used to establish the correlation between mammographic findings, breast density, and suspicion for breast malignancy (higher BIRADS scores). P ≤ 0.05 represented a statistically significant result. RESULTS: A total of 304 patients were involved in this study (age range 20-80 years, mean age 49.0 ± 10.5 years). The patients between 40 and 49 years formed the largest age group with 128 patients (42.4%). Most patients were referred for a breast mass/lump (115/304-38.6%); 56 patients (18.8%) presenting for routine screening. The most common finding on the mammograms was BIRADS 4 in both breasts in 96 patients (31.6%). Most patients had heterogeneous breast density (195 patients - 64.1%). Multivariate logistic regression analysis showed a significant correlation between history of mass, poorly defined margins, and suspicion of malignancy. There was no statistically significant association between abnormal mammographic findings and higher breast density. CONCLUSION: Poorly defined margins were positively correlated with BIRADS ratings suspicious for malignancy. The presence of a breast mass was positively correlated with a higher BIRADS score when other possible cofounding variables were not accounted for. Patient age did not correlate with breast density in this study.

Combination of Peri-Tumoral and Intra-Tumoral Radiomic Features on Bi-Parametric MRI Accurately Stratifies Prostate Cancer Risk: A Multi-Site Study.

Background: Prostate cancer (PCa) influences its surrounding habitat, which tends to manifest as different phenotypic appearances on magnetic resonance imaging (MRI). This region surrounding the PCa lesion, or the peri-tumoral region, may encode useful information that can complement intra-tumoral information to enable better risk stratification. Purpose: To evaluate the role of peri-tumoral radiomic features on bi-parametric MRI (T2-weighted and Diffusion-weighted) to distinguish PCa risk categories as defined by D'Amico Risk Classification System. Materials and Methods: We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients (n = 301 lesions) who underwent 3T multi-parametric MRI prior to biopsy. PCa regions of interest (ROIs) were delineated on MRI by experienced radiologists following which peri-tumoral ROIs were defined. Radiomic features were extracted within the intra- and peri-tumoral ROIs. Radiomic features differentiating low-risk from: (1) high-risk (L-vs.-H), and (2) (intermediate- and high-risk (L-vs.-I + H)) lesions were identified. Using a multi-institutional training cohort of 151 lesions (D1, N = 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, N = 115 patients) were used for independent hold-out validation and were evaluated using Receiver Operating Characteristic (ROC) analysis and compared with PI-RADS v2 scores. Results: Validation on D2 using peri-tumoral radiomics alone resulted in areas under the ROC curve (AUCs) of 0.84 and 0.73 for the L-vs.-H and L-vs.-I + H classifications, respectively. The best combination of intra- and peri-tumoral features resulted in AUCs of 0.87 and 0.75 for the L-vs.-H and L-vs.-I + H classifications, respectively. This combination improved the risk stratification results by 3-6% compared to intra-tumoral features alone. Our radiomics-based model resulted in a 53% accuracy in differentiating L-vs.-H compared to PI-RADS v2 (48%), on the validation set. Conclusion: Our findings suggest that peri-tumoral radiomic features derived from prostate bi-parametric MRI add independent predictive value to intra-tumoral radiomic features for PCa risk assessment.

Quantifying Perfusion Properties with DCE-MRI Using a Dictionary Matching Approach.

Perfusion properties can be estimated from pharmacokinetic models applied to DCE-MRI data using curve fitting algorithms; however, these suffer from drawbacks including the local minimum problem and substantial computational time. Here, a dictionary matching approach is proposed as an alternative. Curve fitting and dictionary matching were applied to simulated data using the dual-input single-compartment model with known perfusion property values and 5 in vivo DCE-MRI datasets. In simulation at SNR 60 dB, the dictionary estimate had a mean percent error of 0.4-1.0% for arterial fraction, 0.5-1.4% for distribution volume, and 0.0% for mean transit time. The curve fitting estimate had a mean percent error of 1.1-2.1% for arterial fraction, 0.5-1.3% for distribution volume, and 0.2-1.8% for mean transit time. In vivo, dictionary matching and curve fitting showed no statistically significant differences in any of the perfusion property measurements in any of the 10 ROIs between the methods. In vivo, the dictionary method performed over 140-fold faster than curve fitting, obtaining whole volume perfusion maps in just over 10 s. This study establishes the feasibility of using a dictionary matching approach as a new and faster way of estimating perfusion properties from pharmacokinetic models in DCE-MRI.

Three-dimensional MR Fingerprinting for Quantitative Breast Imaging.

Purpose To develop a fast three-dimensional method for simultaneous T1 and T2 quantification for breast imaging by using MR fingerprinting. Materials and Methods In this prospective study, variable flip angles and magnetization preparation modules were applied to acquire MR fingerprinting data for each partition of a three-dimensional data set. A fast postprocessing method was implemented by using singular value decomposition. The proposed technique was first validated in phantoms and then applied to 15 healthy female participants (mean age, 24.2 years ± 5.1 [standard deviation]; range, 18-35 years) and 14 female participants with breast cancer (mean age, 55.4 years ± 8.8; range, 39-66 years) between March 2016 and April 2018. The sensitivity of the method to B1 field inhomogeneity was also evaluated by using the Bloch-Siegert method. Results Phantom results showed that accurate and volumetric T1 and T2 quantification was achieved by using the proposed technique. The acquisition time for three-dimensional quantitative maps with a spatial resolution of 1.6 × 1.6 × 3 mm3 was approximately 6 minutes. For healthy participants, averaged T1 and T2 relaxation times for fibroglandular tissues at 3.0 T were 1256 msec ± 171 and 46 msec ± 7, respectively. Compared with normal breast tissues, higher T2 relaxation time (68 msec ± 13) was observed in invasive ductal carcinoma (P < .001), whereas no statistical difference was found in T1 relaxation time (1183 msec ± 256; P = .37). Conclusion A method was developed for breast imaging by using the MR fingerprinting technique, which allows simultaneous and volumetric quantification of T1 and T2 relaxation times for breast tissues. © RSNA, 2018 Online supplemental material is available for this article.

Diagnostic Accuracy of a Rapid Biparametric MRI Protocol for Detection of Histologically Proven Prostate Cancer.

OBJECTIVE: To evaluate the performance of a rapid, low cost, noncontrast MRI examination as a secondary screening tool in detection of clinically significant prostate cancer. METHODS: In this prospective single institution study, 129 patients with elevated prostate-specific antigen levels or abnormal digital rectal examination findings underwent MRI with an abbreviated biparamatric MRI protocol consisting of high-resolution axial T2- and diffusion-weighted images. Index lesions were classified according to modified Prostate Imaging - Reporting and Data System (mPI-RADS) version 2.0. All patients underwent standard transrectal ultrasound-guided biopsy after MRI with the urologist being blinded to MRI results. Subsequently, all patients with suspicious lesions (mPI-RADS 3, 4, or 5) underwent cognitively guided targeted biopsy after discussion of MRI results with the urologist. Sensitivity and negative predictive value for identification of clinically significant prostate cancer (Gleason score 3+4 and above) were determined. RESULTS: Rapid biparametric MRI discovered 176 lesions identified in 129 patients. Rapid MRI detected clinically significant cancers with a sensitivity of 95.1% with a negative predictive value of 95.1% and positive predictive value of 53.2%, leading to a change in management in 10.8% of the patients. False negative rate of biparametric (bp) MRI was 4.7%. CONCLUSION: We found that a bp-MRI examination can detect clinically significant lesions and changed patient management in 10.8% of the patients. A rapid MRI protocol can be used as a useful secondary screening tool in men presenting with suspicion of prostate cancer.

Quantitative perfusion imaging of neoplastic liver lesions: A multi-institution study.

We describe multi-institutional experience using free-breathing, 3D Spiral GRAPPA-based quantitative perfusion MRI in characterizing neoplastic liver masses. 45 patients (age: 48-72 years) were prospectively recruited at University Hospitals, Cleveland, USA on a 3 Tesla (T) MRI, and at Zhongshan Hospital, Shanghai, China on a 1.5 T MRI. Contrast-enhanced volumetric T1-weighted images were acquired and a dual-input single-compartment model used to derive arterial fraction (AF), distribution volume (DV) and mean transit time (MTT) for the lesions and normal parenchyma. The measurements were compared using two-tailed Student's t-test, with Bonferroni correction applied for multiple-comparison testing. 28 hepatocellular carcinoma (HCC) and 17 metastatic lesions were evaluated. No significant difference was noted in perfusion parameters of normal liver parenchyma and neoplastic masses at two centers (p = 0.62 for AF, 0.015 for DV, 0.42 for MTT for HCC, p = 0.13 for AF, 0.97 for DV, 0.78 for MTT for metastases). There was statistically significant difference in AF, DV, and MTT of metastases and AF and DV of HCC compared to normal liver parenchyma (p < 0.5/9 = 0.0055). A statistically significant difference was noted in the MTT of metastases compared to hepatocellular carcinoma (p < 0.001*10-5). In conclusion, 3D Spiral-GRAPPA enabled quantitative free-breathing perfusion MRI exam provides robust perfusion parameters.

Radiomic features on MRI enable risk categorization of prostate cancer patients on active surveillance: Preliminary findings.

BACKGROUND: Radiomic analysis is defined as computationally extracting features from radiographic images for quantitatively characterizing disease patterns. There has been recent interest in examining the use of MRI for identifying prostate cancer (PCa) aggressiveness in patients on active surveillance (AS). PURPOSE: To evaluate the performance of MRI-based radiomic features in identifying the presence or absence of clinically significant PCa in AS patients. STUDY TYPE: Retrospective. SUBJECTS MODEL: MRI/TRUS (transperineal grid ultrasound) fusion-guided biopsy was performed for 56 PCa patients on AS who had undergone prebiopsy. FIELD STRENGTH/SEQUENCE: 3T, T2 -weighted (T2 w) and diffusion-weighted (DW) MRI. ASSESSMENT: A pathologist histopathologically defined the presence of clinically significant disease. A radiologist manually delineated lesions on T2 w-MRs. Then three radiologists assessed MRIs using PIRADS v2.0 guidelines. Tumors were categorized into four groups: MRI-negative-biopsy-negative (Group 1, N = 15), MRI-positive-biopsy-positive (Group 2, N = 16), MRI-negative-biopsy-positive (Group 3, N = 10), and MRI-positive-biopsy-negative (Group 4, N = 15). In all, 308 radiomic features (First-order statistics, Gabor, Laws Energy, and Haralick) were extracted from within the annotated lesions on T2 w images and apparent diffusion coefficient (ADC) maps. The top 10 features associated with clinically significant tumors were identified using minimum-redundancy-maximum-relevance and used to construct three machine-learning models that were independently evaluated for their ability to identify the presence and absence of clinically significant disease. STATISTICAL TESTS: Wilcoxon rank-sum tests with P < 0.05 considered statistically significant. RESULTS: Seven T2 w-based (First-order Statistics, Haralick, Laws, and Gabor) and three ADC-based radiomic features (Laws, Gradient and Sobel) exhibited statistically significant differences (P < 0.001) between malignant and normal regions in the training groups. The three constructed models yielded overall accuracy improvement of 33, 60, 80% and 30, 40, 60% for patients in testing groups, when compared to PIRADS v2.0 alone. DATA CONCLUSION: Radiomic features could help in identifying the presence and absence of clinically significant disease in AS patients when PIRADS v2.0 assessment on MRI contradicted pathology findings of MRI-TRUS prostate biopsies. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Free-Breathing 3D Liver Perfusion Quantification Using a Dual-Input Two-Compartment Model.

The purpose of this study is to test the feasibility of applying a dual-input two-compartment liver perfusion model to patients with different pathologies. A total of 7 healthy subjects and 11 patients with focal liver lesions, including 6 patients with metastatic adenocarcinoma and 5 with hepatocellular carcinoma (HCC), were examined. Liver perfusion values were measured from both focal liver lesions and cirrhotic tissues (from the 5 HCC patients). Compared to results from volunteer livers, significantly higher arterial fraction, fractional volume of the interstitial space, and lower permeability-surface area product were observed for metastatic lesions, and significantly higher arterial fraction and lower vascular transit time were observed for HCCs (P < 0.05). Significantly lower arterial fraction and higher vascular transit time, fractional volume of the vascular space, and fractional volume of the interstitial space were observed for metastases in comparison to HCCs (P < 0.05). For cirrhotic livers, a significantly lower total perfusion, lower fractional volume of the vascular space, higher fractional volume of the interstitial space, and lower permeability-surface area product were noted in comparison to volunteer livers (P < 0.05). Our findings support the possibility of using this model with 3D free-breathing acquisitions for lesion and diffuse liver disease characterization.

Cost-effectiveness of MR Imaging-guided Strategies for Detection of Prostate Cancer in Biopsy-Naive Men.

Purpose To evaluate the cost-effectiveness of multiparametric diagnostic magnetic resonance (MR) imaging examination followed by MR imaging-guided biopsy strategies in the detection of prostate cancer in biopsy-naive men presenting with clinical suspicion of cancer for the first time. Materials and Methods A decision-analysis model was created for biopsy-naive men who had been recommended for prostate biopsy on the basis of abnormal digital rectal examination results or elevated prostate-specific antigen levels (age groups: 41-50 years, 51-60 years, and 61-70 years). The following three major strategies were evaluated: (a) standard transrectal ultrasonography (US)-guided biopsy; (b) diagnostic MR imaging followed by MR imaging-targeted biopsy, with no biopsy performed if MR imaging findings were negative; and (c) diagnostic MR imaging followed by MR imaging-targeted biopsy, with a standard biopsy performed when MR imaging findings were negative. The following three MR imaging-guided biopsy strategies were further evaluated in each MR imaging category: (a) biopsy with cognitive guidance, (b) biopsy with MR imaging/US fusion guidance, and (c) in-gantry MR imaging-guided biopsy. Model parameters were derived from the literature. The primary outcome measure was net health benefit (NHB), which was measured as quality-adjusted life-years (QALYs) gained or lost by investing resources in a new strategy compared with a standard strategy at a willingness-to-pay (WTP) threshold of $50 000 per QALY gained. Probabilistic sensitivity analysis was performed by using Monte Carlo simulations. Results Noncontrast MR imaging followed by cognitively guided MR biopsy (no standard biopsy if MR imaging findings were negative) was the most cost-effective approach, yielding an additional NHB of 0.198 QALY compared with the standard biopsy approach. Noncontrast MR imaging followed by in-gantry MR imaging-guided biopsy (no standard biopsy if MR imaging findings were negative) led to the highest NHB gain of 0.251 additional QALY compared with the standard biopsy strategy. All MR imaging strategies were cost-effective in 94.05% of Monte Carlo simulations. Analysis by age groups yielded similar results. Conclusion MR imaging-guided strategies for the detection of prostate cancer were cost-effective compared with the standard biopsy strategy in a decision-analysis model. © RSNA, 2017 Online supplemental material is available for this article.

Development of a Combined MR Fingerprinting and Diffusion Examination for Prostate Cancer.

Purpose To develop and evaluate an examination consisting of magnetic resonance (MR) fingerprinting-based T1, T2, and standard apparent diffusion coefficient (ADC) mapping for multiparametric characterization of prostate disease. Materials and Methods This institutional review board-approved, HIPAA-compliant retrospective study of prospectively collected data included 140 patients suspected of having prostate cancer. T1 and T2 mapping was performed with fast imaging with steady-state precession-based MR fingerprinting with ADC mapping. Regions of interest were drawn by two independent readers in peripheral zone lesions and normal-appearing peripheral zone (NPZ) tissue identified on clinical images. T1, T2, and ADC were recorded for each region. Histopathologic correlation was based on systematic transrectal biopsy or cognitively targeted biopsy results, if available. Generalized estimating equations logistic regression was used to assess T1, T2, and ADC in the differentiation of (a) cancer versus NPZ, (b) cancer versus prostatitis, (c) prostatitis versus NPZ, and (d) high- or intermediate-grade tumors versus low-grade tumors. Analysis was performed for all lesions and repeated in a targeted biopsy subset. Discriminating ability was evaluated by using the area under the receiver operating characteristic curve (AUC). Results In this study, 109 lesions were analyzed, including 39 with cognitively targeted sampling. T1, T2, and ADC from cancer (mean, 1628 msec ± 344, 73 msec ± 27, and 0.773 × 10-3 mm2/sec ± 0.331, respectively) were significantly lower than those from NPZ (mean, 2247 msec ± 450, 169 msec ± 61, and 1.711 × 10-3 mm2/sec ± 0.269) (P < .0001 for each) and together produced the best separation between these groups (AUC = 0.99). ADC and T2 together produced the highest AUC of 0.83 for separating high- or intermediate-grade tumors from low-grade cancers. T1, T2, and ADC in prostatitis (mean, 1707 msec ± 377, 79 msec ± 37, and 0.911 × 10-3 mm2/sec ± 0.239) were significantly lower than those in NPZ (P < .0005 for each). Interreader agreement was excellent, with an intraclass correlation coefficient greater than 0.75 for both T1 and T2 measurements. Conclusion This study describes the development of a rapid MR fingerprinting- and diffusion-based examination for quantitative characterization of prostatic tissue. © RSNA, 2017 Online supplemental material is available for this article.

Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi-institutional study.

PURPOSE: To evaluate in a multi-institutional study whether radiomic features useful for prostate cancer (PCa) detection from 3 Tesla (T) multi-parametric MRI (mpMRI) in the transition zone (TZ) differ from those in the peripheral zone (PZ). MATERIALS AND METHODS: 3T mpMRI, including T2-weighted (T2w), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced MRI (DCE-MRI), were retrospectively obtained from 80 patients at three institutions. This study was approved by the institutional review board of each participating institution. First-order statistical, co-occurrence, and wavelet features were extracted from T2w MRI and ADC maps, and contrast kinetic features were extracted from DCE-MRI. Feature selection was performed to identify 10 features for PCa detection in the TZ and PZ, respectively. Two logistic regression classifiers used these features to detect PCa and were evaluated by area under the receiver-operating characteristic curve (AUC). Classifier performance was compared with a zone-ignorant classifier. RESULTS: Radiomic features that were identified as useful for PCa detection differed between TZ and PZ. When classification was performed on a per-voxel basis, a PZ-specific classifier detected PZ tumors on an independent test set with significantly higher accuracy (AUC = 0.61-0.71) than a zone-ignorant classifier trained to detect cancer throughout the entire prostate (P < 0.05). When classifiers were evaluated on MRI data from multiple institutions, statistically similar AUC values (P > 0.14) were obtained for all institutions. CONCLUSION: A zone-aware classifier significantly improves the accuracy of cancer detection in the PZ. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:184-193.

MR Fingerprinting for Rapid Quantitative Abdominal Imaging.

PURPOSE: To develop a magnetic resonance (MR) "fingerprinting" technique for quantitative abdominal imaging. MATERIALS AND METHODS: This HIPAA-compliant study had institutional review board approval, and informed consent was obtained from all subjects. To achieve accurate quantification in the presence of marked B0 and B1 field inhomogeneities, the MR fingerprinting framework was extended by using a two-dimensional fast imaging with steady-state free precession, or FISP, acquisition and a Bloch-Siegert B1 mapping method. The accuracy of the proposed technique was validated by using agarose phantoms. Quantitative measurements were performed in eight asymptomatic subjects and in six patients with 20 focal liver lesions. A two-tailed Student t test was used to compare the T1 and T2 results in metastatic adenocarcinoma with those in surrounding liver parenchyma and healthy subjects. RESULTS: Phantom experiments showed good agreement with standard methods in T1 and T2 after B1 correction. In vivo studies demonstrated that quantitative T1, T2, and B1 maps can be acquired within a breath hold of approximately 19 seconds. T1 and T2 measurements were compatible with those in the literature. Representative values included the following: liver, 745 msec ± 65 (standard deviation) and 31 msec ± 6; renal medulla, 1702 msec ± 205 and 60 msec ± 21; renal cortex, 1314 msec ± 77 and 47 msec ± 10; spleen, 1232 msec ± 92 and 60 msec ± 19; skeletal muscle, 1100 msec ± 59 and 44 msec ± 9; and fat, 253 msec ± 42 and 77 msec ± 16, respectively. T1 and T2 in metastatic adenocarcinoma were 1673 msec ± 331 and 43 msec ± 13, respectively, significantly different from surrounding liver parenchyma relaxation times of 840 msec ± 113 and 28 msec ± 3 (P < .0001 and P < .01) and those in hepatic parenchyma in healthy volunteers (745 msec ± 65 and 31 msec ± 6, P < .0001 and P = .021, respectively). CONCLUSION: A rapid technique for quantitative abdominal imaging was developed that allows simultaneous quantification of multiple tissue properties within one 19-second breath hold, with measurements comparable to those in published literature.

Multiparametric MR Imaging in Abdominal Malignancies.

Modern MR imaging protocols can yield both anatomic and functional information for the assessment of hepatobiliary and pancreatic malignancies. Diffusion-weighted imaging is fully integrated into state-of-the-art protocols for tumor detection, characterization, and therapy monitoring. Hepatobiliary contrast agents have gained ground in the evaluation of focal liver lesions during the last years. Perfusion MR imaging is expected to have a central role for monitoring therapy in body tumors treated with antivascular drugs. Approaches such as Magnetic resonance (MR) elastography and (1)H-MR spectroscopy are still confined to research centers, but with the potential to grow in a short time frame.

Multidetector computed tomography of temporomandibular joint: A road less travelled.

This article reviews the imaging anatomy of temporomandibular joint (TMJ), describes the technique of multi-detector computed tomography (MDCT) of the TMJ, and describes in detail various osseous pathologic afflictions affecting the joint. Traumatic injuries affecting the mandibular condyle are most common, followed by joint ankylosis as a sequel to arthritis. The congenital anomalies are less frequent, hemifacial microsomia being the most commonly encountered anomaly involving the TMJ. Neoplastic afflictions of TMJ are distinctly uncommon, osteochondroma being one of the most common lesions. MDCT enables comprehensive evaluation of osseous afflictions of TMJ, and is a valuable tool for surgical planning. Sagittal, coronal and 3D reformatted images well depict osseous TMJ lesions, and their relationship to adjacent structures.