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Bougia CΚ, Astrakas L, Pappa O, Maliakas V, Sofikitis N, Argyropoulou MI, Tsili AC. Diffusion tensor imaging and fiber tractography of the normal epididymis. Abdom Radiol (NY) 2024:10.1007/s00261-024-04372-y. [PMID: 38836882 DOI: 10.1007/s00261-024-04372-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 06/06/2024]
Abstract
PURPOSE To evaluate the feasibility of diffusion tensor imaging (DTI) and fiber tractography (FT) of the normal epididymis and to determine normative apparent diffusion coefficient (ADC) and fractional anisotropy (FA) values. METHODS Twenty-eight healthy volunteers underwent MRI of the scrotum, including DTI on a 3.0 T system. For each anatomic part of the epididymis (head, body and tail) free-hand regions of interest were drawn and the mean ADC and FA were measured by two radiologists in consensus. Parametric statistical tests were used to determine intersubject differences in ADC and FA between the anatomic parts of each normal epididymis and between bilateral epididymides. Fiber tracts of the epididymis were reconstructed using the MR Diffusion tool. RESULTS The mean ADC and FA of the normal epididymis was 1.31 × 10-3 mm2/s and 0.20, respectively. No differences in ADC (p = 0.736) and FA (p = 0.628) between the anatomic parts of each normal epididymis were found. Differences (p = 0.020) were observed in FA of the body between the right and the left epididymis. FT showed the fiber tracts of the normal epididymis. Main study's limitations include the following: small number of participants with narrow age range, absence of histologic confirmation and lack of quantitative assessment of the FT reconstructions. CONCLUSION DTI and FT of the normal epididymis is feasible and allow the noninvasive assessment of the structural and geometric organization of the organ.
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Affiliation(s)
- Christina Κ Bougia
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece
| | - Loukas Astrakas
- Department of Medical Physics, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece
| | - Ourania Pappa
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece
| | - Vasileios Maliakas
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece
- Department of Clinical Radiology, University Hospital of Ioannina, St. Niarchos, 45500, Ioannina, Greece
| | - Nikolaos Sofikitis
- Department of Urology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece
| | - Maria I Argyropoulou
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece
| | - Athina C Tsili
- Department of Clinical Radiology, School of Health Sciences, Faculty of Medicine, University of Ioannina, 45110, Ioannina, Greece.
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Surveillance Value of Apparent Diffusion Coefficient Maps: Multiparametric MRI in Active Surveillance of Prostate Cancer. Cancers (Basel) 2023; 15:cancers15041128. [PMID: 36831471 PMCID: PMC9953850 DOI: 10.3390/cancers15041128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 02/12/2023] Open
Abstract
BACKGROUND This study aims to establish the value of apparent diffusion coefficient maps and other magnetic resonance sequences for active surveillance of prostate cancer. The study included 530 men with an average age of 66, who were under surveillance for prostate cancer. We have used multiparametric magnetic resonance imaging with subsequent transperineal biopsy (TPB) to verify the imaging findings. RESULTS We have observed a level of agreement of 67.30% between the apparent diffusion coefficient (ADC) maps, other magnetic resonance sequences, and the biopsy results. The sensitivity of the apparent diffusion coefficient is 97.14%, and the specificity is 37.50%. According to our data, apparent diffusion coefficient is the most accurate sequence, followed by diffusion imaging in prostate cancer detection. CONCLUSIONS Based on our findings we advocate that the apparent diffusion coefficient should be included as an essential part of magnetic resonance scanning protocols for prostate cancer in at least bi-parametric settings. The best option will be apparent diffusion coefficient combined with diffusion imaging and T2 sequences. Further large-scale prospective controlled studies are required to define the precise role of multiparametric and bi-parametric magnetic resonance in the active surveillance of prostate cancer.
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Singh D, Kumar V, Das CJ, Singh A, Mehndiratta A. Characterisation of prostate cancer using texture analysis for diagnostic and prognostic monitoring. NMR IN BIOMEDICINE 2021; 34:e4495. [PMID: 33638244 DOI: 10.1002/nbm.4495] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 02/06/2021] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
Automated classification of significant prostate cancer (PCa) using MRI plays a potential role in assisting in clinical decision-making. Multiparametric MRI using a machine-aided approach is a better step to improve the overall accuracy of diagnosis of PCa. The objective of this study was to develop and validate a framework for differentiating Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2) grades (grade 2 to grade 5) of PCa using texture features and machine learning (ML) methods with diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC). The study cohort included an MRI dataset of 59 patients with clinically proven PCa. Regions of interest (ROIs) for a total of 435 lesions were delineated from the segmented peripheral zones of DWI and ADC. Six texture methods comprising 98 texture features in total (49 each of DWI and ADC) were extracted from lesion ROIs. Random forest (RF) and correlation-based feature selection methods were applied on feature vectors to select the best features for classification. Two ML classifiers, support vector machine (SVM) and K-nearest neighbour, were used and validated by 10-fold cross-validation. The proposed framework achieved high diagnostic performance with a sensitivity of 85.25% ± 3.84%, specificity of 95.71% ± 1.96%, accuracy of 84.90% ± 3.37% and area under the receiver-operating characteristic curve of 0.98 for PI-RADS v2 grades (2 to 5) classification using the RF feature selection method and Gaussian SVM classifier with combined features of DWI + ADC. The proposed computer-assisted framework can distinguish between PCa lesions with different aggressiveness based on PI-RADS v2 standards using texture analysis to improve the efficiency of PCa diagnostic performance.
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Affiliation(s)
- Dharmesh Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Virendra Kumar
- Department of NMR, All India Institute of Medical Sciences, New Delhi, India
| | - Chandan J Das
- Department of Radiodiagnosis, All India Institute of Medical Sciences, New Delhi, India
| | - Anup Singh
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
| | - Amit Mehndiratta
- Centre for Biomedical Engineering, Indian Institute of Technology Delhi, New Delhi, India
- Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India
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Abreu-Gomez J, Walker D, Alotaibi T, McInnes MDF, Flood TA, Schieda N. Effect of observation size and apparent diffusion coefficient (ADC) value in PI-RADS v2.1 assessment category 4 and 5 observations compared to adverse pathological outcomes. Eur Radiol 2020; 30:4251-4261. [PMID: 32211965 DOI: 10.1007/s00330-020-06725-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/03/2019] [Accepted: 02/05/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE To compare observation size and apparent diffusion coefficient (ADC) values in Prostate Imaging Reporting and Data System (PI-RADS) v2.1 category 4 and 5 observations to adverse pathological features. MATERIALS AND METHODS With institutional review board approval, 267 consecutive men with 3-T MRI before radical prostatectomy (RP) between 2012 and 2018 were evaluated by two blinded radiologists who assigned PI-RADS v2.1 scores. Discrepancies were resolved by consensus. A third blinded radiologist measured observation size and ADC (ADC.mean, ADC.min [lowest ADC within an observation], ADC.ratio [ADC.mean/ADC.peripheral zone {PZ}]). Size and ADC were compared to pathological stage and Gleason score (GS) using t tests, ANOVA, Pearson correlation, and receiver operating characteristic (ROC) analysis. RESULTS Consensus review identified 267 true positive category 4 and 5 observations representing 83.1% (222/267) PZ and 16.9% (45/267) transition zone (TZ) tumors. Inter-observer agreement for PI-RADS v2.1 scoring was moderate (K = 0.45). Size was associated with extra-prostatic extension (EPE) (19 ± 8 versus 14 ± 6 mm, p < 0.001) and seminal vesicle invasion (SVI) (24 ± 9 versus 16 ± 7 mm, p < 0.001). Size ≥ 15 mm optimized the accuracy for EPE with area under the ROC curve (AUC) and sensitivity/specificity of 0.68 (CI 0.62-0.75) and 63.2%/65.6%. Size ≥ 19 mm optimized the accuracy for SVI with AUC/sensitivity/specificity of 0.75 (CI 0.66-0.83)/69.4%/70.6%. ADC metrics were not associated with pathological stage. Larger observation size (p = 0.032), lower ADC.min (p = 0.010), and lower ADC.ratio (p = 0.010) were associated with higher GS. Size correlated better to higher Gleason scores (p = 0.002) compared to ADC metrics (p = 0.09-0.11). CONCLUSION Among PI-RADS v2.1 category 4 and 5 observations, size was associated with higher pathological stage whereas ADC metrics were not. Size, ADC.minimum, and ADC.ratio differed in tumors stratified by Gleason score. KEY POINTS • Among PI-RADS category 4 and 5 observations, size but not ADC can differentiate between tumors by pathological stage. • An observation size threshold of 15 mm and 19 mm optimized the accuracy for diagnosis of extra-prostatic extension and seminal vesicle invasion. • Among PI-RADS category 4 and 5 observations, size, ADC.minimum, and ADC.ratio differed comparing tumors by Gleason score.
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Affiliation(s)
- Jorge Abreu-Gomez
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, C1 Radiology, Ottawa, Ontario, K1Y 4E9, Canada
| | - Daniel Walker
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, C1 Radiology, Ottawa, Ontario, K1Y 4E9, Canada
| | - Tareq Alotaibi
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, C1 Radiology, Ottawa, Ontario, K1Y 4E9, Canada
| | - Matthew D F McInnes
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, C1 Radiology, Ottawa, Ontario, K1Y 4E9, Canada
| | - Trevor A Flood
- Department of Anatomical Pathology, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada
| | - Nicola Schieda
- Department of Medical Imaging, The Ottawa Hospital, University of Ottawa, 1053 Carling Avenue, C1 Radiology, Ottawa, Ontario, K1Y 4E9, Canada.
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Tomita H, Soga S, Suyama Y, Ito K, Asano T, Shinmoto H. Analysis of Diffusion-weighted MR Images Based on a Gamma Distribution Model to Differentiate Prostate Cancers with Different Gleason Score. Magn Reson Med Sci 2019; 19:40-47. [PMID: 30918223 PMCID: PMC7067910 DOI: 10.2463/mrms.mp.2018-0124] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Purpose: Prostate cancer management includes identification of clinically significant cancers that may require curative treatment. Statistical models based on gamma distribution can describe diffusion signal decay curves of prostate cancer. The purpose of this study was to evaluate the ability of parameters obtained with the gamma model in differentiating prostate cancers with different Gleason score values. Methods: This study included 155 patients with prostate cancer who underwent multiparametric magnetic resonance imaging prior to prostate biopsy (127 patients) or radical prostatectomy (28 patients) between January 2015 and June 2017; 159 foci of prostate cancer were included in our study. We compared cases scored as Gleason score (GS) 3 + 3 and GS ≥ 3 + 4, and analyzed cases scored as GS ≤ 3+ 4 and GS ≥ 4 + 3 based on the gamma model (Frac < 1.0, Frac < 0.8, Frac < 0.5, Frac < 0.3, and Frac > 3.0), and apparent diffusion coefficient (ADC). Results: Among 159 cancerous lesions in 155 patients, 13 (8.2%) were GS 3 + 3 prostate cancers, 51 (32.0%) were GS 3 + 4 prostate cancers, 30 (18.2%) were GS 4 + 3 cancers, and 65 (40.9%) were GS ≥ 4 + 4 cancers. Frac < 0.3, Frac < 0.5, Frac < 0.8, and Frac < 1.0 were significantly higher and ADC values were significantly lower in GS ≥ 4 + 3 cancers than in GS ≤ 3 + 4 cancers (P < 0.01, P < 0.01, P < 0.01, P = 0.01, and P < 0.01, respectively). With receiver operating characteristic (ROC) analysis, Frac < 0.3 and Frac < 0.5 had significantly greater area under the ROC curve for discriminating GS ≥ 4 + 3 cancers from GS ≤ 3 + 4 cancers than ADC (P = 0.03, P < 0.01, respectively). Conclusion: Frac < 0.3 and Frac < 0.5 showed higher diagnostic performance than ADC for differentiating GS ≥ 4 + 3 from GS ≤ 3 + 4 cancers. The gamma model may add additional value in discrimination of tumor grades.
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Affiliation(s)
- Hiroko Tomita
- Department of Radiology, National Defense Medical College
| | | | - Yohsuke Suyama
- Department of Radiology, National Defense Medical College
| | - Keiichi Ito
- Department of Urology, National Defense Medical College
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Abstract
Although testicular carcinoma represents approximately only 1% of solid neoplasms in men, it is the most common malignancy between young men. The two main histologic categories are testicular germ cell tumors (TGCTs), including seminomas and nonseminomas, accounting for 90-95% of testicular neoplasms and sex cord-stromal tumors. Scrotal MRI, including a multiparametric protocol, has been proposed as a valuable supplemental imaging technique in the investigation of testicular pathology. Recently, the Scrotal and Penile Imaging Working Group appointed by the board of the European Society of Urogenital Radiology has produced recommendations on when to perform scrotal MRI. Regarding intratesticular masses, MRI of the scrotum may be used for their characterization, when US findings are indeterminate and for local staging of TGCTs, when organ-sparing surgery is planned. Differentiation between seminomas and nonseminomas is possible based on MRI features, when clinically needed. Scrotal MRI may also help in differentiating between TGCTs and nongerm cell tumors. Functional information based on diffusion-weighted imaging and dynamic contrast-enhanced MRI data improve testicular mass lesion characterization. Preliminary observations on diffusion tensor imaging, magnetization transfer imaging, and proton MR spectroscopy bring about new data in the understanding of testicular microstructure and pathophysiology.
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Affiliation(s)
- Athina C Tsili
- Department of Clinical Radiology, Medical School, University of Ioannina, University Campus, 45110, Ioannina, Greece.
| | - Nikolaos Sofikitis
- Department of Urology, Medical School, University of Ioannina, University Campus, 45110, Ioannina, Greece
| | - Efrosyni Stiliara
- Department of Clinical Radiology, Medical School, University of Ioannina, University Campus, 45110, Ioannina, Greece
| | - Maria I Argyropoulou
- Department of Clinical Radiology, Medical School, University of Ioannina, University Campus, 45110, Ioannina, Greece
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Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, Huang W, Noworolski SM, Young RJ, Shiroishi MS, Kim H, Coolens C, Laue H, Chung C, Rosen M, Boss M, Jackson EF. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging 2018; 49:e101-e121. [PMID: 30451345 DOI: 10.1002/jmri.26518] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 09/06/2018] [Accepted: 09/06/2018] [Indexed: 12/14/2022] Open
Abstract
Physiological properties of tumors can be measured both in vivo and noninvasively by diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging. Although these techniques have been used for more than two decades to study tumor diffusion, perfusion, and/or permeability, the methods and studies on how to reduce measurement error and bias in the derived imaging metrics is still lacking in the literature. This is of paramount importance because the objective is to translate these quantitative imaging biomarkers (QIBs) into clinical trials, and ultimately in clinical practice. Standardization of the image acquisition using appropriate phantoms is the first step from a technical performance standpoint. The next step is to assess whether the imaging metrics have clinical value and meet the requirements for being a QIB as defined by the Radiological Society of North America's Quantitative Imaging Biomarkers Alliance (QIBA). The goal and mission of QIBA and the National Cancer Institute Quantitative Imaging Network (QIN) initiatives are to provide technical performance standards (QIBA profiles) and QIN tools for producing reliable QIBs for use in the clinical imaging community. Some of QIBA's development of quantitative diffusion-weighted imaging and dynamic contrast-enhanced QIB profiles has been hampered by the lack of literature for repeatability and reproducibility of the derived QIBs. The available research on this topic is scant and is not in sync with improvements or upgrades in MRI technology over the years. This review focuses on the need for QIBs in oncology applications and emphasizes the importance of the assessment of their reproducibility and repeatability. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019;49:e101-e121.
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Affiliation(s)
- Amita Shukla-Dave
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, New York, USA.,Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Nancy A Obuchowski
- Department of Quantitative Health Sciences, Cleveland Clinic Foundation, Cleveland, Ohio, USA
| | - Thomas L Chenevert
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Sachin Jambawalikar
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Lawrence H Schwartz
- Department of Radiology, Columbia University Irving Medical Center, New York, New York, USA
| | - Dariya Malyarenko
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Wei Huang
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon, USA
| | - Susan M Noworolski
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA
| | - Robert J Young
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Mark S Shiroishi
- Division of Neuroradiology, Department of Radiology, University of Southern California, Los Angeles, California, USA
| | - Harrison Kim
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Catherine Coolens
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Canada
| | | | - Caroline Chung
- Department of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Mark Rosen
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Michael Boss
- Applied Physics Division, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Edward F Jackson
- Departments of Medical Physics, Radiology, and Human Oncology, University of Wisconsin School of Medicine, Madison, Wisconsin, USA
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Reproducibility of Index Lesion Size and Mean Apparent Diffusion Coefficient Values Measured by Prostate Multiparametric MRI: Correlation With Whole-Mount Sectioning of Specimens. AJR Am J Roentgenol 2018; 211:783-788. [DOI: 10.2214/ajr.17.19172] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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9
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New prostate cancer prognostic grade group (PGG): Can multiparametric MRI (mpMRI) accurately separate patients with low-, intermediate-, and high-grade cancer? Abdom Radiol (NY) 2018; 43:702-712. [PMID: 28721479 DOI: 10.1007/s00261-017-1255-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
PURPOSE Our objective is to determine the accuracy of multiparametric MRI (mpMRI) in predicting pathologic grade of prostate cancer (PCa) after radical prostatectomy (RP) using simple apparent diffusion coefficient metrics and, specifically, whether mpMRI can accurately separate disease into one of two risk categories (low vs. higher grade) or one of three risk categories (low, intermediate, or high grade) corresponding to the new prognostic grade group (PGG) criteria. METHODS This retrospective, HIPAA-compliant, IRB-approved study included 140 patients with PCa who underwent 3 T mpMRI with endorectal coil and transrectal ultrasound-guided (TRUS-G) biopsy before RP. MpMRI was used to classify lesions using a two-tier (low-grade/PGG 1 vs. high-grade/PGG 2-5) or a three-tier system (low-grade/PGG 1 vs. intermediate-grade/PGG 2 vs. high-grade/PGG 3-5). Accuracy of mpMRI was compared against RP for each system. RESULTS The predictive accuracy of mpMRI using the two-tier system is higher than when using three-tier system (0.77 and 0.45, respectively). There were similar rates of undergrading between mpMRI and TRUS-G biopsy compared to RP (16% & 21%; respectively); rate of overgrading was higher for mpMRI vs. TRUS-G biopsy compared to RP (42% & 17%, respectively). When mpMRI and TRUS-G biopsy are combined, rate of undergrading is 1.4% and overgrading is 11%. CONCLUSIONS MpMRI predictive accuracy is higher when using a two-tier vs. a three-tier system, suggesting that advanced metrics may be necessary to delineate intermediate- from high-grade disease. Rates of under- and overgrading decreased when mpMRI and TRUS-G biopsy are combined, suggesting that these techniques may be complementary in predicting tumor grade.
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