Literature DB >> 29725743

3T multiparametric MR imaging, PIRADSv2-based detection of index prostate cancer lesions in the transition zone and the peripheral zone using whole mount histopathology as reference standard.

Nazanin Hajarol Asvadi1, Sohrab Afshari Mirak2, Amirhossein Mohammadian Bajgiran1, Pooria Khoshnoodi1, Pornphan Wibulpolprasert3, Daniel Margolis4, Anthony Sisk5, Robert E Reiter6, Steven S Raman1,6.   

Abstract

PURPOSE: To evaluate 3T mpMRI characteristics of transition zone and peripheral zone index prostate cancer lesions stratified by Gleason Score and PI-RADSv2 with whole mount histopathology correlation.
METHODS: An institution review board-approved, HIPAA-compliant single-arm observational study of 425 consecutive men with 3T mpMRI prior to radical prostatectomy from December 2009 to October 2016 was performed. A genitourinary radiologist and a genitourinary pathologist matched all lesions detected on whole mount histopathology with lesions concordant for size and location on 3T mpMRI. Differences in clinical, MRI parameters, and histopathology between transition zone and peripheral zone were determined and analyzed with χ2 and Mann-Whitney U test. AUC was measured.
RESULTS: 3T mpMRI detected 248/323 (76.7%) index lesions in peripheral zone and 75/323 (23.2%) in transition zone. Transition zone prostate cancer had higher median prostate-specific antigen (p = 0.001), larger tumor on 3T mpMRI (p = 0.001), lower proportions of PI-RADSv2 category 4 and 5 (p < 0.001), and lower pathological stage (p = 0.055) compared to peripheral zone prostate cancer. No significant differences were detected in prostate-specific antigen density, preoperative biopsy, and pathology Gleason Scores. After adjusting for significant variables from univariate analysis including prostate volume, tumor volume, prostate-specific antigen, PI-RADSv2 category, AUC for predicting clinically significant tumor in transition zone and peripheral zone were 0.80 and 0.72, respectively (p = 0.36).
CONCLUSIONS: The diagnostic performance of PI-RADSv2 for clinically significant transition and peripheral zone prostate cancer was similar. However, there was a lower portion of PI-RADSv2 4 and 5 lesions in transition zone compared to peripheral zone.

Entities:  

Keywords:  Gleason score; Multiparametric magnetic resonance imaging; PI-RADSv2; Prostate cancer

Year:  2018        PMID: 29725743      PMCID: PMC6922085          DOI: 10.1007/s00261-018-1598-9

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  13 in total

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Journal:  BJU Int       Date:  2010-11-02       Impact factor: 5.588

2.  Multifocality and prostate cancer detection by multiparametric magnetic resonance imaging: correlation with whole-mount histopathology.

Authors:  Jesse D Le; Nelly Tan; Eugene Shkolyar; David Y Lu; Lorna Kwan; Leonard S Marks; Jiaoti Huang; Daniel J A Margolis; Steven S Raman; Robert E Reiter
Journal:  Eur Urol       Date:  2014-09-23       Impact factor: 20.096

3.  Interobserver Reproducibility of the PI-RADS Version 2 Lexicon: A Multicenter Study of Six Experienced Prostate Radiologists.

Authors:  Andrew B Rosenkrantz; Luke A Ginocchio; Daniel Cornfeld; Adam T Froemming; Rajan T Gupta; Baris Turkbey; Antonio C Westphalen; James S Babb; Daniel J Margolis
Journal:  Radiology       Date:  2016-04-01       Impact factor: 11.105

4.  An analysis of 148 consecutive transition zone cancers: clinical and histological characteristics.

Authors:  M Noguchi; T A Stamey; J E Neal; C E Yemoto
Journal:  J Urol       Date:  2000-06       Impact factor: 7.450

5.  Multiparametric Magnetic Resonance Imaging for Discriminating Low-Grade From High-Grade Prostate Cancer.

Authors:  Eline K Vos; Thiele Kobus; Geert J S Litjens; Thomas Hambrock; Christina A Hulsbergen-van de Kaa; Jelle O Barentsz; Marnix C Maas; Tom W J Scheenen
Journal:  Invest Radiol       Date:  2015-08       Impact factor: 6.016

6.  In-Bore 3-T MR-guided Transrectal Targeted Prostate Biopsy: Prostate Imaging Reporting and Data System Version 2-based Diagnostic Performance for Detection of Prostate Cancer.

Authors:  Nelly Tan; Wei-Chan Lin; Pooria Khoshnoodi; Nazanin H Asvadi; Jeffrey Yoshida; Daniel J A Margolis; David S K Lu; Holden Wu; Kyung Hyun Sung; David Y Lu; Jaioti Huang; Steven S Raman
Journal:  Radiology       Date:  2016-11-18       Impact factor: 11.105

7.  Prostate Cancer: Interobserver Agreement and Accuracy with the Revised Prostate Imaging Reporting and Data System at Multiparametric MR Imaging.

Authors:  Berrend G Muller; Joanna H Shih; Sandeep Sankineni; Jamie Marko; Soroush Rais-Bahrami; Arvin Koruthu George; Jean J M C H de la Rosette; Maria J Merino; Bradford J Wood; Peter Pinto; Peter L Choyke; Baris Turkbey
Journal:  Radiology       Date:  2015-06-18       Impact factor: 11.105

8.  Biologic differences between peripheral and transition zone prostate cancer.

Authors:  J Joy Lee; I-Chun Thomas; Rosalie Nolley; Michelle Ferrari; James D Brooks; John T Leppert
Journal:  Prostate       Date:  2014-10-18       Impact factor: 4.104

9.  Do prostatic transition zone tumors have a distinct morphology?

Authors:  Joaquin J Garcia; Hikmat A Al-Ahmadie; Anuradha Gopalan; Satish K Tickoo; Peter T Scardino; Victor E Reuter; Samson W Fine
Journal:  Am J Surg Pathol       Date:  2008-11       Impact factor: 6.394

10.  Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging.

Authors:  Marc R Engelbrecht; Henkjan J Huisman; Robert J F Laheij; Gerrit J Jager; Geert J L H van Leenders; Christina A Hulsbergen-Van De Kaa; Jean J M C H de la Rosette; Johan G Blickman; Jelle O Barentsz
Journal:  Radiology       Date:  2003-08-27       Impact factor: 11.105

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1.  Detectability of prostate cancer in different parts of the gland with 3-Tesla multiparametric magnetic resonance imaging: correlation with whole-mount histopathology.

Authors:  Katsuhiro Ito; Akihiro Furuta; Akira Kido; Yuki Teramoto; Shusuke Akamatsu; Naoki Terada; Toshinari Yamasaki; Takahiro Inoue; Osamu Ogawa; Takashi Kobayashi
Journal:  Int J Clin Oncol       Date:  2019-12-02       Impact factor: 3.402

2.  Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.

Authors:  Adeel Ahmed Abbasi; Lal Hussain; Imtiaz Ahmed Awan; Imran Abbasi; Abdul Majid; Malik Sajjad Ahmed Nadeem; Quratul-Ain Chaudhary
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Review 3.  Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer.

Authors:  Niklas Harland; Arnulf Stenzl; Tilman Todenhöfer
Journal:  World J Mens Health       Date:  2020-06-24       Impact factor: 5.400

  3 in total

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