Publications by authors named "Andrei Purysko"

65 Publications

ACR Appropriateness Criteria® Renal Failure.

J Am Coll Radiol 2021 May;18(5S):S174-S188

Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama, Chair, ACR Appropriateness Committee.

Renal failure can be divided into acute kidney injury and chronic kidney disease. Both are common and result in increased patient morbidity and mortality. The etiology is multifactorial and differentiation of acute kidney injury from chronic kidney disease includes clinical evaluation, laboratory tests, and imaging. The main role of imaging is to detect treatable causes of renal failure such as ureteral obstruction or renovascular disease and to evaluate renal size and morphology. Ultrasound is the modality of choice for initial imaging, with duplex Doppler reserved for suspected renal artery stenosis or thrombosis. CT and MRI may be appropriate, particularly for urinary tract obstruction. However, the use of iodinated and gadolinium-based contrast should be evaluated critically depending on specific patient factors and cost-benefit ratio. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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http://dx.doi.org/10.1016/j.jacr.2021.02.019DOI Listing
May 2021

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study.

NPJ Precis Oncol 2021 May 3;5(1):35. Epub 2021 May 3.

Department of Urology, Case Western Reserve University, Cleveland, OH, USA.

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests.
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http://dx.doi.org/10.1038/s41698-021-00174-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093226PMC
May 2021

A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI.

EBioMedicine 2021 Jan 13;63:103163. Epub 2020 Dec 13.

Center for Computational Imaging and Personalized Diagnostics, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, USA. Electronic address:

Background: We developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher.

Methods: A retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D, N = 127).

Findings: "RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79)."

Interpretation: RadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy.

Funding: The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.
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http://dx.doi.org/10.1016/j.ebiom.2020.103163DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7744939PMC
January 2021

Influence of Enema and Dietary Restrictions on Prostate MR Image Quality: A Multireader Study.

Acad Radiol 2020 Nov 5. Epub 2020 Nov 5.

Imaging institute, Cleveland Clinic, 9500 Euclid Avenue, JB-322, Cleveland, OH 44145.

Purpose: To evaluate the effect of enema and dietary restrictions on prostate MR image quality metrics and to assess inter-reader agreement for these metrics.

Methods: This retrospective study included 195 men divided into groups based on their compliance with preparation instructions before prostate MRI (Enema + Diet, n = 98; Enema, n = 42; Diet, n = 35; Control [no compliance], n = 20). Four readers independently assessed six image quality metrics on a 5-point scale. Between-group comparisons were made using Wilcoxon rank sum test. Inter-reader agreement was calculated using Fleiss' kappa.

Results: Compared with the Control group, image quality with respect to rectal stool/gas, distortion of diffusion-weighted images, overall image quality, and confidence in assessment was higher in the Enema + Diet, Enema, and Diet groups (p  < 0.05 for all comparisons). The Enema + Diet and Enema groups had significantly higher scores than the Diet group for rectal stool/gas (p < 0.001 and 0.005, respectively). The Enema + Diet and Diet groups had higher scores than the Control group for rectal peristalsis (p = 0.027 and 0.009, respectively), but there were no significant differences in motion artifacts on T2-weighted images. Agreement among readers was fair, with kappa values ranging from 0.25 to 0.37.

Conclusion: Enema and dietary restriction can improve the quality of prostate MRI by decreasing rectal distension and distortion of diffusion-weighted images and by increasing reader confidence in image assessment. Inter-reader agreement using subjective criteria for analysis of MRI quality is fair.
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http://dx.doi.org/10.1016/j.acra.2020.10.019DOI Listing
November 2020

ACR Appropriateness Criteria® Indeterminate Renal Mass.

J Am Coll Radiol 2020 Nov;17(11S):S415-S428

Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama.

Renal masses are increasingly detected in asymptomatic individuals as incidental findings. CT and MRI with intravenous contrast and a dedicated multiphase protocol are the mainstays of evaluation for indeterminate renal masses. A single-phase postcontrast dual-energy CT can be useful when a dedicated multiphase renal protocol CT is not available. Contrast-enhanced ultrasound with microbubble agents is a useful alternative for characterizing renal masses, especially for patients in whom iodinated CT contrast or gadolinium-based MRI contrast is contraindicated. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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http://dx.doi.org/10.1016/j.jacr.2020.09.010DOI Listing
November 2020

Update: PI-RADS Version 2.1-A Pictorial Update.

Radiographics 2020 Nov-Dec;40(7):E33-E37

From the Section of Abdominal Imaging and Nuclear Radiology Department, Cleveland Clinic, Imaging Institute, 9500 Euclid Ave, JB3, Cleveland, OH 44195 (A.S.P.); Department of Radiology, NYU Langone Medical Center, New York, NY (A.B.R.); Molecular Imaging Program, National Cancer Institute, Bethesda, Md (I.B.T.); and Department of Radiology and Radiological Sciences, Johns Hopkins University, Baltimore, Md (K.J.M.).

Articles in the Update section provide current knowledge to supplement or update information found in full-length articles previously published in . Authors of the previously published article provide a brief synopsis that emphasizes important new information such as technological advances, revised imaging protocols, new clinical guidelines involving imaging, or updated classification schemes. Articles in this section are published solely online and are linked to the original article.
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http://dx.doi.org/10.1148/rg.2020190207DOI Listing
November 2020

Data Augmentation and Transfer Learning to Improve Generalizability of an Automated Prostate Segmentation Model.

AJR Am J Roentgenol 2020 12 14;215(6):1403-1410. Epub 2020 Oct 14.

Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bldg 10, Rm B3B85, Bethesda MD 20892.

Deep learning applications in radiology often suffer from overfitting, limiting generalization to external centers. The objective of this study was to develop a high-quality prostate segmentation model capable of maintaining a high degree of performance across multiple independent datasets using transfer learning and data augmentation. A retrospective cohort of 648 patients who underwent prostate MRI between February 2015 and November 2018 at a single center was used for training and validation. A deep learning approach combining 2D and 3D architecture was used for training, which incorporated transfer learning. A data augmentation strategy was used that was specific to the deformations, intensity, and alterations in image quality seen on radiology images. Five independent datasets, four of which were from outside centers, were used for testing, which was conducted with and without fine-tuning of the original model. The Dice similarity coefficient was used to evaluate model performance. When prostate segmentation models utilizing transfer learning were applied to the internal validation cohort, the mean Dice similarity coefficient was 93.1 for whole prostate and 89.0 for transition zone segmentations. When the models were applied to multiple test set cohorts, the improvement in performance achieved using data augmentation alone was 2.2% for the whole prostate models and 3.0% for the transition zone segmentation models. However, the best test-set results were obtained with models fine-tuned on test center data with mean Dice similarity coefficients of 91.5 for whole prostate segmentation and 89.7 for transition zone segmentation. Transfer learning allowed for the development of a high-performing prostate segmentation model, and data augmentation and fine-tuning approaches improved performance of a prostate segmentation model when applied to datasets from external centers.
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http://dx.doi.org/10.2214/AJR.19.22347DOI Listing
December 2020

PI-RADS Version 2.1: A Critical Review, From the Special Series on Radiology Reporting and Data Systems.

AJR Am J Roentgenol 2021 01 19;216(1):20-32. Epub 2020 Nov 19.

Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA.

PI-RADS version 2.1 updates the technical parameters for multiparametric MRI (mpMRI) of the prostate and revises the imaging interpretation criteria while maintaining the framework introduced in version 2. These changes have been considered an improvement, although some issues remain unresolved, and new issues have emerged. Areas for improvement discussed in this review include the need for more detailed mpMRI protocols with optimization for 1.5-T and 3-T systems; lack of validation of revised transition zone interpretation criteria and need for clarifications of the revised DWI and dynamic contrast-enhanced imaging criteria and central zone (CZ) assessment; the need for systematic evaluation and reporting of background changes in signal intensity in the prostate that can negatively affect cancer detection; creation of a new category for lesions that do not fit into the PI-RADS assessment categories (i.e., PI-RADS M category); inclusion of quantitative parameters beyond size to evaluate lesion aggressiveness; adjustments to the structured report template, including standardized assessment of the risk of extraprostatic extension; development of parameters for image quality and performance control; and suggestions for expansion of the system to other indications (e.g., active surveillance and recurrence).
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http://dx.doi.org/10.2214/AJR.20.24495DOI Listing
January 2021

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

Cancers (Basel) 2020 Aug 6;12(8). Epub 2020 Aug 6.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.

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. : 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. : We studied a retrospective, HIPAA-compliant, 4-institution cohort of 231 PCa patients ( = 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, 116 patients), machine learning classifiers were trained using peri- and intra-tumoral features individually and in combination. The remaining 150 lesions (D2, 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. : 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. : 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.
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http://dx.doi.org/10.3390/cancers12082200DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7465024PMC
August 2020

Multicenter Multireader Evaluation of an Artificial Intelligence-Based Attention Mapping System for the Detection of Prostate Cancer With Multiparametric MRI.

AJR Am J Roentgenol 2020 10 5;215(4):903-912. Epub 2020 Aug 5.

Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD.

The purpose of this study was to evaluate in a multicenter dataset the performance of an artificial intelligence (AI) detection system with attention mapping compared with multiparametric MRI (mpMRI) interpretation in the detection of prostate cancer. MRI examinations from five institutions were included in this study and were evaluated by nine readers. In the first round, readers evaluated mpMRI studies using the Prostate Imaging Reporting and Data System version 2. After 4 weeks, images were again presented to readers along with the AI-based detection system output. Readers accepted or rejected lesions within four AI-generated attention map boxes. Additional lesions outside of boxes were excluded from detection and categorization. The performances of readers using the mpMRI-only and AI-assisted approaches were compared. The study population included 152 case patients and 84 control patients with 274 pathologically proven cancer lesions. The lesion-based AUC was 74.9% for MRI and 77.5% for AI with no significant difference ( = 0.095). The sensitivity for overall detection of cancer lesions was higher for AI than for mpMRI but did not reach statistical significance (57.4% vs 53.6%, = 0.073). However, for transition zone lesions, sensitivity was higher for AI than for MRI (61.8% vs 50.8%, = 0.001). Reading time was longer for AI than for MRI (4.66 vs 4.03 minutes, < 0.001). There was moderate interreader agreement for AI and MRI with no significant difference (58.7% vs 58.5%, = 0.966). Overall sensitivity was only minimally improved by use of the AI system. Significant improvement was achieved, however, in the detection of transition zone lesions with use of the AI system at the cost of a mean of 40 seconds of additional reading time.
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http://dx.doi.org/10.2214/AJR.19.22573DOI Listing
October 2020

Radiomic Texture and Shape Descriptors of the Rectal Environment on Post-Chemoradiation T2-Weighted MRI are Associated with Pathologic Tumor Stage Regression in Rectal Cancers: A Retrospective, Multi-Institution Study.

Cancers (Basel) 2020 Jul 24;12(8). Epub 2020 Jul 24.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.

(1) : The relatively poor expert restaging accuracy of MRI in rectal cancer after neoadjuvant chemoradiation may be due to the difficulties in visual assessment of residual tumor on post-treatment MRI. In order to capture underlying tissue alterations and morphologic changes in rectal structures occurring due to the treatment, we hypothesized that radiomics texture and shape descriptors of the rectal environment (e.g., wall, lumen) on post-chemoradiation T2-weighted (T2w) MRI may be associated with tumor regression after neoadjuvant chemoradiation therapy (nCRT). (2) : A total of 94 rectal cancer patients were retrospectively identified from three collaborating institutions, for whom a 1.5 or 3T T2w MRI was available after nCRT and prior to surgical resection. The rectal wall and the lumen were annotated by an expert radiologist on all MRIs, based on which 191 texture descriptors and 198 shape descriptors were extracted for each patient. (3) : Top-ranked features associated with pathologic tumor-stage regression were identified via cross-validation on a discovery set ( = 52, 1 institution) and evaluated via discriminant analysis in hold-out validation ( = 42, 2 institutions). The best performing features for distinguishing low (ypT0-2) and high (ypT3-4) pathologic tumor stages after nCRT comprised directional gradient texture expression and morphologic shape differences in the entire rectal wall and lumen. Not only were these radiomic features found to be resilient to variations in magnetic field strength and expert segmentations, a quadratic discriminant model combining them yielded consistent performance across multiple institutions (hold-out AUC of 0.73). (4) : Radiomic texture and shape descriptors of the rectal wall from post-treatment T2w MRIs may be associated with low and high pathologic tumor stage after neoadjuvant chemoradiation therapy and generalized across variations between scanners and institutions.
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http://dx.doi.org/10.3390/cancers12082027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7463898PMC
July 2020

Clinical utility of PSAD combined with PI-RADS category for the detection of clinically significant prostate cancer.

Urol Oncol 2020 11 21;38(11):846.e9-846.e16. Epub 2020 Jun 21.

Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland OH.

Purpose: The goal of this study was to determine the predictive value of prostate-specific antigen density (PSAD) plus Prostate Imaging Reporting and Data System (PI-RADS) category for the detection of clinically significant prostate cancer.

Materials And Methods: This retrospective study included 526 men without known prostate cancer (initial diagnosis group) and 133 men with prostate cancer grade group 1 (active surveillance group) who underwent magnetic resonance imaging-guided and/or systematic prostate biopsy procedures between August 2014 and October 2018. Prostate specific antigen (PSA), PSAD, and PI-RADS category were entered into logistic regression models for predicting clinically significant prostate cancer (grade group ≥2) at biopsy. Receiver operating characteristic curve analysis was performed to assess model accuracy.

Results: The area under the curve (AUC) increased when PSAD was combined with PI-RADS in the initial diagnosis group (difference in AUC = 0.031; 95% confidence interval: 0.012, 0.050; P = 0.002) but not in the active surveillance group (difference in AUC = 0.016; 95% confidence interval: -0.040, 0.071; P = 0.579). When a PSAD threshold of 0.15 was applied, the frequency of clinically significant prostate cancer in patients with a PI-RADS score of 3 or lower decreased from 9.8% to 5.6% in the initial diagnosis group and from 10.7% to 2.7% in the active surveillance group.

Conclusions: The addition of PSAD improves the predictive performance of PI-RADS in men without known prostate cancer. A PSAD threshold of 0.15 can help to minimize the number of missed clinically significant prostate cancer cases in men with a PI-RADS score of 3 or lower who decide to defer biopsy.
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http://dx.doi.org/10.1016/j.urolonc.2020.05.024DOI Listing
November 2020

ACR Appropriateness Criteria® Hematuria.

J Am Coll Radiol 2020 May;17(5S):S138-S147

Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama.

Hematuria is a common reason for patients to be referred for imaging of the urinary tract. All patients diagnosed with hematuria should undergo a thorough history and physical examination, urinalysis, and serologic testing prior to any initial imaging. Ultrasound, CT, and MRI are the most common imaging modalities used to evaluate hematuria. This document discusses the following clinical scenarios for hematuria: initial imaging of microhematuria without risk factors or history of recent vigorous exercise, or presence of infection, or viral illness, or present or recent menstruation; initial imaging of microhematuria in patients with known risk factors and no history of recent vigorous exercise, or presence of infection, or viral illness, or present or recent menstruation or renal parenchymal disease; initial imaging of microhematuria in the pregnant patient and initial imaging of gross hematuria. Follow-up of normal or abnormal findings is beyond the scope of this review. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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http://dx.doi.org/10.1016/j.jacr.2020.01.028DOI Listing
May 2020

Radiomic Features of Primary Rectal Cancers on Baseline T -Weighted MRI Are Associated With Pathologic Complete Response to Neoadjuvant Chemoradiation: A Multisite Study.

J Magn Reson Imaging 2020 11 26;52(5):1531-1541. Epub 2020 Mar 26.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA.

Background: Twenty-five percent of rectal adenocarcinoma patients achieve pathologic complete response (pCR) to neoadjuvant chemoradiation and could avoid proctectomy. However, pretreatment clinical or imaging markers are lacking in predicting response to chemoradiation. Radiomic texture features from MRI have recently been associated with therapeutic response in other cancers.

Purpose: To construct a radiomics texture model based on pretreatment MRI for identifying patients who will achieve pCR to neoadjuvant chemoradiation in rectal cancer, including validation across multiple scanners and sites.

Study Type: Retrospective.

Subjects: In all, 104 rectal cancer patients staged with MRI prior to long-course chemoradiation followed by proctectomy; curated from three institutions.

Field Strength/sequence: 1.5T-3.0T, axial higher resolution T -weighted turbo spin echo sequence.

Assessment: Pathologic response was graded on postsurgical specimens. In total, 764 radiomic features were extracted from single-slice sections of rectal tumors on processed pretreatment T -weighted MRI.

Statistical Tests: Three feature selection schemes were compared for identifying radiomic texture descriptors associated with pCR via a discovery cohort (one site, N = 60, cross-validation). The top-selected radiomic texture features were used to train and validate a random forest classifier model for pretreatment identification of pCR (two external sites, N = 44). Model performance was evaluated via area under the curve (AUC), accuracy, sensitivity, and specificity.

Results: Laws kernel responses and gradient organization features were most associated with pCR (P ≤ 0.01); as well as being commonly identified across all feature selection schemes. The radiomics model yielded a discovery AUC of 0.699 ± 0.076 and a hold-out validation AUC of 0.712 with 70.5% accuracy (70.0% sensitivity, 70.6% specificity) in identifying pCR. Radiomic texture features were resilient to variations in magnetic field strength as well as being consistent between two different expert annotations. Univariate analysis revealed no significant associations of baseline clinicopathologic or MRI findings with pCR (P = 0.07-0.96).

Data Conclusion: Radiomic texture features from pretreatment MRIs may enable early identification of potential pCR to neoadjuvant chemoradiation, as well as generalize across sites.

Level Of Evidence: 3 TECHNICAL EFFICACY STAGE: 2.
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http://dx.doi.org/10.1002/jmri.27140DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7529659PMC
November 2020

Round table: arguments against using multiparametric prostate MRI protocols.

Abdom Radiol (NY) 2020 12;45(12):3997-4002

Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, 9500 Euclid Avenue, Mail Code JB-322, Cleveland, OH, 44145, USA.

Biparametric MRI (bpMRI), which uses only T2-weighted imaging and diffusion-weighted imaging, continues to gain support for the detection of prostate cancer, as this imaging technique offers many benefits over traditional mpMRI. However, the Prostate Imaging Reporting and Data System (PI-RADS) v2.1 document released in 2019 emphasized that mpMRI is still preferred over bpMRI in most clinical scenarios. As one article in a series of four providing arguments for and against using mpMRI and bpMRI protocols, this paper provides arguments against using mpMRI. Within this article, we discuss recent data suggesting equivalent performance between bpMRI and mpMRI in the detection of prostate cancer. The limited utility of dynamic contrast enhancement in the evaluation of prostate cancer according to the PI-RADS v2.1 document is also reviewed. Finally, we detail the large financial and time costs, legal and logistical issues, and potential for patient harm that must be considered with the administration of contrast.
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http://dx.doi.org/10.1007/s00261-020-02456-zDOI Listing
December 2020

Magnetic Resonance Imaging of Prostate Adenocarcinoma: Detection and Staging.

Top Magn Reson Imaging 2020 Feb;29(1):17-30

Department of Abdominal Imaging and Nuclear Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH.

Prostate cancer (PCa) is common among men worldwide and is a major cause of morbidity and mortality. The detection of PCa has historically followed a stepwise process of prostate-specific antigen screening followed with systematic transrectal ultrasound-guided biopsy. Magnetic resonance imaging (MRI), utilizing a set of sequences to assess morphology and function, has gained clinical acceptance to detect, characterize, and stage PCa. The Prostate Imaging - Reporting and Data System has helped facilitate the standardization of reporting across institutions and increased adoption of this method. In this review, we will (1) discuss the strengths and weaknesses of conventional diagnostic methods; (2) describe the clinical utility of prostate MRI, specifically addressing its uses in the detection and staging of PCa; and (3) list important technical parameters required for state-of-the-art prostate MRI.
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http://dx.doi.org/10.1097/RMR.0000000000000226DOI Listing
February 2020

Optimum Imaging Strategies for Advanced Prostate Cancer: ASCO Guideline.

J Clin Oncol 2020 06 15;38(17):1963-1996. Epub 2020 Jan 15.

Memorial Sloan Kettering Cancer Center, New York, NY.

Purpose: Provide evidence- and expert-based recommendations for optimal use of imaging in advanced prostate cancer. Due to increases in research and utilization of novel imaging for advanced prostate cancer, this guideline is intended to outline techniques available and provide recommendations on appropriate use of imaging for specified patient subgroups.

Methods: An Expert Panel was convened with members from ASCO and the Society of Abdominal Radiology, American College of Radiology, Society of Nuclear Medicine and Molecular Imaging, American Urological Association, American Society for Radiation Oncology, and Society of Urologic Oncology to conduct a systematic review of the literature and develop an evidence-based guideline on the optimal use of imaging for advanced prostate cancer. Representative index cases of various prostate cancer disease states are presented, including suspected high-risk disease, newly diagnosed treatment-naïve metastatic disease, suspected recurrent disease after local treatment, and progressive disease while undergoing systemic treatment. A systematic review of the literature from 2013 to August 2018 identified fully published English-language systematic reviews with or without meta-analyses, reports of rigorously conducted phase III randomized controlled trials that compared ≥ 2 imaging modalities, and noncomparative studies that reported on the efficacy of a single imaging modality.

Results: A total of 35 studies met inclusion criteria and form the evidence base, including 17 systematic reviews with or without meta-analysis and 18 primary research articles.

Recommendations: One or more of these imaging modalities should be used for patients with advanced prostate cancer: conventional imaging (defined as computed tomography [CT], bone scan, and/or prostate magnetic resonance imaging [MRI]) and/or next-generation imaging (NGI), positron emission tomography [PET], PET/CT, PET/MRI, or whole-body MRI) according to the clinical scenario.
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http://dx.doi.org/10.1200/JCO.19.02757DOI Listing
June 2020

ACR Appropriateness Criteria® Post-Treatment Follow-up and Active Surveillance of Clinically Localized Renal Cell Cancer.

J Am Coll Radiol 2019 Nov;16(11S):S399-S416

Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama.

Renal cell carcinoma (RCC) accounts for most malignant renal tumors and is considered the most lethal of all urologic cancers. For follow-up of patients with treated or untreated RCC and those with neoplasms suspected to represent RCC, radiologic imaging is the most useful component of surveillance, as most relapses and cases of disease progression are identified when patients are asymptomatic. Understanding the strengths and limitations of the various imaging modalities for the detection of disease, recurrence, or progression is important when planning follow-up regimens. This publication addresses the appropriate imaging examinations for asymptomatic patients who have been treated for RCC with radical or partial nephrectomy, or ablative therapies. It also discusses the appropriate imaging examinations for asymptomatic patients with localized biopsy-proven or suspected RCC undergoing active surveillance. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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http://dx.doi.org/10.1016/j.jacr.2019.05.022DOI Listing
November 2019

Addition of magnetic resonance imaging to real time trans-rectal ultrasound-based treatment planning for prostate implants.

J Contemp Brachytherapy 2019 Aug 29;11(4):361-369. Epub 2019 Aug 29.

Department of Radiation Oncology, Cleveland Clinic, Cleveland, Ohio, United States.

Purpose: The greater soft tissue contrast of magnetic resonance imaging (MRI) allows improved accuracy in prostate contouring compared to transrectal ultrasound (TRUS) and helps in identifying specific regions within the prostate. This study attempts to evaluate the potential benefit of MRI-TRUS fusion in treatment planning for more accurate prostate contouring and tumor dose escalation.

Material And Methods: 14 patients with previous MRI-guided prostate biopsy and an low-dose-rate (LDR) permanent prostate seed implant have been selected. The prostate and tumor (5 patients) were contoured on the MRI images by a radiologist. The prostate was also contoured on TRUS images during LDR procedure together by a urologist and radiation oncologist. MRI and TRUS images were rigidly fused to compare prostate contours in MRI and TRUS. Prostate was then re-contoured by the radiation oncologist using this fusion. Moreover, V, V, and D differences were evaluated for localized tumor compared to prostate with negative values indicating cold tumor regions. These cases were re-planned to simulate dose escalation.

Results: The prostate volume was contoured 8 ±10% smaller in TRUS images, compared to MRI images. The mean percent difference in tumor (compared to prostate) V was 0.3 ±-0.4%, V was -0.7 ±-24.8%, and D was 0.2 ±-12.1%. For the posteriorly located tumors (2 cases), V was 0.0 ±-0.3%, D was 9.5 ±-3.0%, and V was 26.1 ±-5.4%. For anteriorly located tumors (3 cases), V was 0.4 ±-0.4%, D was -6.0 ±-11.9%, and V was -18.5 ±-14.4% (became 15.6 ±14.6% after re-plan).

Conclusions: The MRI-TRUS image fusion is a feasible tool for the visualization of the prostate gland, particularly at the apex and base of the gland. Tumor identification presents the potential for dose escalation using fusion, especially for anteriorly located tumors.
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http://dx.doi.org/10.5114/jcb.2019.87189DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6737565PMC
August 2019

EDITORIAL COMMENT.

Authors:
Andrei S Purysko

Urology 2019 09;131:45

Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH; Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, OH.

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http://dx.doi.org/10.1016/j.urology.2019.04.041DOI Listing
September 2019

Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

J Med Imaging (Bellingham) 2019 Apr 14;6(2):024502. Epub 2019 Jun 14.

Case Western Reserve University, Department of Biomedical Engineering, Cleveland, Ohio, United States.

Recent advances in the field of radiomics have enabled the development of a number of prognostic and predictive imaging-based tools for a variety of diseases. However, wider clinical adoption of these tools is contingent on their generalizability across multiple sites and scanners. This may be particularly relevant in the context of radiomic features derived from T1- or T2-weighted magnetic resonance images (MRIs), where signal intensity values are known to lack tissue-specific meaning and vary based on differing acquisition protocols between institutions. We present the first empirical study of benchmarking five different radiomic feature families in terms of both reproducibility and discriminability in a multisite setting, specifically, for identifying prostate tumors in the peripheral zone on MRI. Our cohort comprised 147 patient T2-weighted MRI datasets from four different sites, all of which are first preprocessed to correct for acquisition-related artifacts such as bias field, differing voxel resolutions, and intensity drift (nonstandardness). About 406 three-dimensional voxel-wise radiomic features from five different families (gray, Haralick, gradient, Laws, and Gabor) were evaluated in a cross-site setting to determine (a) how reproducible they are within a relatively homogeneous nontumor tissue region and (b) how well they could discriminate tumor regions from nontumor regions. Our results demonstrate that a majority of the popular Haralick features are reproducible in over 99% of all cross-site comparisons, as well as achieve excellent cross-site discriminability (classification accuracy of ). By contrast, a majority of Laws features are highly variable across sites (reproducible in of all cross-site comparisons) as well as resulting in low cross-site classifier accuracies ( ), likely due to a large number of noisy filter responses that can be extracted. These trends suggest that only a subset of radiomic features and associated parameters may be both reproducible and discriminable enough for use within machine learning classifier schemes.
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http://dx.doi.org/10.1117/1.JMI.6.2.024502DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6566001PMC
April 2019

Editorial Comment.

Authors:
Andrei S Purysko

Urology 2019 05;127:72-73

Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH.

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http://dx.doi.org/10.1016/j.urology.2019.01.036DOI Listing
May 2019

Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.

Eur Radiol 2019 Sep 7;29(9):4861-4870. Epub 2019 Mar 7.

Glickman Urological and Kidney Institute, Cleveland Clinic, 9500 Euclid Ave, Cleveland, OH, 44195, USA.

Objectives: We sought to evaluate the correlation between MRI phenotypes of prostate cancer as defined by PI-RADS v2 and the Decipher Genomic Classifier (used to estimate the risk of early metastases).

Methods: This single-center, retrospective study included 72 nonconsecutive men with prostate cancer who underwent MRI before radical prostatectomy performed between April 2014 and August 2017 and whose MRI registered lesions were microdissected from radical prostatectomy specimens and then profiled using Decipher (89 lesions; 23 MRI invisible [PI-RADS v2 scores ≤ 2] and 66 MRI visible [PI-RADS v2 scores ≥ 3]). Linear regression analysis was used to assess clinicopathologic and MRI predictors of Decipher results; correlation coefficients (r) were used to quantify these associations. AUC was used to determine whether PI-RADS v2 could accurately distinguish between low-risk (Decipher score < 0.45) and intermediate-/high-risk (Decipher score ≥ 0.45) lesions.

Results: MRI-visible lesions had higher Decipher scores than MRI-invisible lesions (mean difference 0.22; 95% CI 0.13, 0.32; p < 0.0001); most MRI-invisible lesions (82.6%) were low risk. PI-RADS v2 had moderate correlation with Decipher (r = 0.54) and had higher accuracy (AUC 0.863) than prostate cancer grade groups (AUC 0.780) in peripheral zone lesions (95% CI for difference 0.01, 0.15; p = 0.018).

Conclusions: MRI phenotypes of prostate cancer are positively correlated with Decipher risk groups. Although PI-RADS v2 can accurately distinguish between lesions classified by Decipher as low or intermediate/high risk, some lesions classified as intermediate/high risk by Decipher are invisible on MRI.

Key Points: • MRI phenotypes of prostate cancer as defined by PI-RADS v2 positively correlated with a genomic classifier that estimates the risk of early metastases. • Most but not all MRI-invisible lesions had a low risk for early metastases according to the genomic classifier. • MRI could be used in conjunction with genomic assays to identify lesions that may carry biological potential for early metastases.
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http://dx.doi.org/10.1007/s00330-019-06114-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6684343PMC
September 2019

Comparing radiomic classifiers and classifier ensembles for detection of peripheral zone prostate tumors on T2-weighted MRI: a multi-site study.

BMC Med Imaging 2019 02 28;19(1):22. Epub 2019 Feb 28.

Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.

Background: For most computer-aided diagnosis (CAD) problems involving prostate cancer detection via medical imaging data, the choice of classifier has been largely ad hoc, or been motivated by classifier comparison studies that have involved large synthetic datasets. More significantly, it is currently unknown how classifier choices and trends generalize across multiple institutions, due to heterogeneous acquisition and intensity characteristics (especially when considering MR imaging data). In this work, we empirically evaluate and compare a number of different classifiers and classifier ensembles in a multi-site setting, for voxel-wise detection of prostate cancer (PCa) using radiomic texture features derived from high-resolution in vivo T2-weighted (T2w) MRI.

Methods: Twelve different supervised classifier schemes: Quadratic Discriminant Analysis (QDA), Support Vector Machines (SVMs), naïve Bayes, Decision Trees (DTs), and their ensemble variants (bagging, boosting), were compared in terms of classification accuracy as well as execution time. Our study utilized 85 prostate cancer T2w MRI datasets acquired from across 3 different institutions (1 for discovery, 2 for independent validation), from patients who later underwent radical prostatectomy. Surrogate ground truth for disease extent on MRI was established by expert annotation of pre-operative MRI through spatial correlation with corresponding ex vivo whole-mount histology sections. Classifier accuracy in detecting PCa extent on MRI on a per-voxel basis was evaluated via area under the ROC curve.

Results: The boosted DT classifier yielded the highest cross-validated AUC (= 0.744) for detecting PCa in the discovery cohort. However, in independent validation, the boosted QDA classifier was identified as the most accurate and robust for voxel-wise detection of PCa extent (AUCs of 0.735, 0.683, 0.768 across the 3 sites). The next most accurate and robust classifier was the single QDA classifier, which also enjoyed the advantage of significantly lower computation times compared to any of the other methods.

Conclusions: Our results therefore suggest that simpler classifiers (such as QDA and its ensemble variants) may be more robust, accurate, and efficient for prostate cancer CAD problems, especially in the context of multi-site validation.
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http://dx.doi.org/10.1186/s12880-019-0308-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396464PMC
February 2019

A unique case of ectopic Cushing's syndrome from a thymic neuroendocrine carcinoma.

Endocrinol Diabetes Metab Case Rep 2019 Feb 22;2019. Epub 2019 Feb 22.

Departments of Endocrinology, Diabetes & Metabolism, Cleveland Clinic, Cleveland, Ohio, USA.

Ectopic adrenocorticotropic hormone (ACTH) production leading to ectopic ACTH syndrome accounts for a small proportion of all Cushing's syndrome (CS) cases. Thymic neuroendocrine tumors are rare neoplasms that may secrete ACTH leading to rapid development of hypercortisolism causing electrolyte and metabolic abnormalities, uncontrolled hypertension and an increased risk for opportunistic infections. We present a unique case of a patient who presented with a mediastinal mass, revealed to be an ACTH-secreting thymic neuroendocrine tumor (NET) causing ectopic CS. As the diagnosis of CS from ectopic ACTH syndrome (EAS) remains challenging, we emphasize the necessity for high clinical suspicion in the appropriate setting, concordance between biochemical, imaging and pathology findings, along with continued vigilant monitoring for recurrence after definitive treatment. Learning points: Functional thymic neuroendocrine tumors are exceedingly rare. Ectopic Cushing's syndrome secondary to thymic neuroendocrine tumors secreting ACTH present with features of hypercortisolism including electrolyte and metabolic abnormalities, uncontrolled hypertension and hyperglycemia, and opportunistic infections. The ability to undergo surgery and completeness of resection are the strongest prognostic factors for improved overall survival; however, the recurrence rate remains high. A high degree of initial clinical suspicion followed by vigilant monitoring is required for patients with this challenging disease.
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http://dx.doi.org/10.1530/EDM-19-0002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6391898PMC
February 2019

Editorial comment.

Authors:
Andrei S Purysko

Urology 2019 01;123:196-197

Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH.

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http://dx.doi.org/10.1016/j.urology.2018.07.068DOI Listing
January 2019

ACR Appropriateness Criteria Acute Pyelonephritis.

J Am Coll Radiol 2018 Nov;15(11S):S232-S239

Specialty Chair, University of Alabama at Birmingham, Birmingham, Alabama.

Pyelonephritis refers to infection involving the renal parenchyma and renal pelvis. In most patients, uncomplicated pyelonephritis is diagnosed clinically and responds quickly to appropriate antibiotic treatment. If treatment is delayed, the patient is immunocompromised, or for other reasons, microabscesses that form during the acute phase of pyelonephritis may coalesce, forming a renal abscess. Patients with underlying diabetes are more vulnerable to complications, including emphysematous pyelonephritis in addition to abscess formation. Additionally, diabetics may not have the typical flank tenderness that helps to differentiate pyelonephritis from a lower urinary tract infection. Additional high-risk populations may include those with anatomic abnormalities of the urinary tract, vesicoureteral reflux, obstruction, pregnancy, nosocomial infection, or infection by treatment-resistant pathogens. Treatment goals include symptom relief, elimination of infection to avoid renal damage, and identification of predisposing factors to avoid future recurrences. The primary imaging modalities used in patients with pyelonephritis are CT, MRI, and ultrasound. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.
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http://dx.doi.org/10.1016/j.jacr.2018.09.011DOI Listing
November 2018

Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

Oncotarget 2018 Sep 18;9(73):33804-33817. Epub 2018 Sep 18.

Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development.
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http://dx.doi.org/10.18632/oncotarget.26100DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6173466PMC
September 2018

Technique of Multiparametric MR Imaging of the Prostate.

Urol Clin North Am 2018 Aug;45(3):427-438

Department of Radiology, New York University Langone Medical Center, 660 First Avenue, New York, NY 10016, USA.

Multiparametric MR imaging provides detailed anatomic assessment of the prostate as well as information that allows the detection and characterization of prostate cancer. To obtain high-quality MR imaging of the prostate, radiologists must understand sequence optimization to overcome commonly encountered technical challenges. This review discusses the techniques that are used in state-of-the-art MR imaging of the prostate, including imaging protocols, hardware considerations, and important aspects of patient preparation, with an emphasis on the recommendations provided in the prostate imaging-reporting and data system version 2 guidelines.
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http://dx.doi.org/10.1016/j.ucl.2018.03.008DOI Listing
August 2018

Multiparametric Magnetic Resonance Imaging in the Evaluation of Prostate Cancer Recurrence.

Semin Roentgenol 2018 Jul 5;53(3):234-246. Epub 2018 Apr 5.

Section of Abdominal Imaging and Nuclear Radiology Department, Imaging Institute, Cleveland Clinic, Cleveland, OH. Electronic address:

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http://dx.doi.org/10.1053/j.ro.2018.04.002DOI Listing
July 2018