Literature DB >> 30066169

Predictive role of PI-RADSv2 and ADC parameters in differentiating Gleason pattern 3 + 4 and 4 + 3 prostate cancer.

Francesco Alessandrino1,2, Mehdi Taghipour3, Elmira Hassanzadeh3,4, Alireza Ziaei3, Mark Vangel3, Andriy Fedorov3, Clare M Tempany3, Fiona M Fennessy3,5.   

Abstract

PURPOSE: To compare the predictive roles of qualitative (PI-RADSv2) and quantitative assessment (ADC metrics), in differentiating Gleason pattern (GP) 3 + 4 from the more aggressive GP 4 + 3 prostate cancer (PCa) using radical prostatectomy (RP) specimen as the reference standard.
METHODS: We retrospectively identified treatment-naïve peripheral (PZ) and transitional zone (TZ) Gleason Score 7 PCa patients who underwent multiparametric 3T prostate MRI (DWI with b value of 0,1400 and where unavailable, 0,500) and subsequent RP from 2011 to 2015. For each lesion identified on MRI, a PI-RADSv2 score was assigned by a radiologist blinded to pathology data. A PI-RADSv2 score ≤ 3 was defined as "low risk," a PI-RADSv2 score ≥ 4 as "high risk" for clinically significant PCa. Mean tumor ADC (ADCT), ADC of adjacent normal tissue (ADCN), and ADCratio (ADCT/ADCN) were calculated. Stepwise regression analysis using tumor location, ADCT and ADCratio, b value, low vs. high PI-RADSv2 score was performed to differentiate GP 3 + 4 from 4 + 3.
RESULTS: 119 out of 645 cases initially identified met eligibility requirements. 76 lesions were GP 3 + 4, 43 were 4 + 3. ADCratio was significantly different between the two GP groups (p = 0.001). PI-RADSv2 score ("low" vs. "high") was not significantly different between the two GP groups (p = 0.17). Regression analysis selected ADCT (p = 0.03) and ADCratio (p = 0.0007) as best predictors to differentiate GP 4 + 3 from 3 + 4. Estimated sensitivity, specificity, and accuracy of the predictive model in differentiating GP 4 + 3 from 3 + 4 were 37, 82, and 66%, respectively.
CONCLUSIONS: ADC metrics could differentiate GP 3 + 4 from 4 + 3 PCa with high specificity and moderate accuracy while PI-RADSv2, did not differentiate between these patterns.

Entities:  

Keywords:  Apparent diffusion coefficient; Diffusion-weighted imaging; Gleason score; Magnetic resonance imaging; PI-RADSv2; Prostate cancer

Mesh:

Year:  2019        PMID: 30066169      PMCID: PMC6349548          DOI: 10.1007/s00261-018-1718-6

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  7 in total

1.  Biparametric prostate MRI: impact of a deep learning-based software and of quantitative ADC values on the inter-reader agreement of experienced and inexperienced readers.

Authors:  Stefano Cipollari; Martina Pecoraro; Alì Forookhi; Ludovica Laschena; Marco Bicchetti; Emanuele Messina; Sara Lucciola; Carlo Catalano; Valeria Panebianco
Journal:  Radiol Med       Date:  2022-09-17       Impact factor: 6.313

2.  Development of a glycoproteomic strategy to detect more aggressive prostate cancer using lectin-immunoassays for serum fucosylated PSA.

Authors:  Ce Wang; Naseruddin Höti; Tung-Shing Mamie Lih; Lori J Sokoll; Rui Zhang; Zhen Zhang; Hui Zhang; Daniel W Chan
Journal:  Clin Proteomics       Date:  2019-04-06       Impact factor: 3.988

3.  DWI-related texture analysis for prostate cancer: differences in correlation with histological aggressiveness and data repeatability between peripheral and transition zones.

Authors:  Chie Tsuruta; Kenji Hirata; Kohsuke Kudo; Naoya Masumori; Masamitsu Hatakenaka
Journal:  Eur Radiol Exp       Date:  2022-01-12

4.  Non-timely clinically applicable ADC ratio in prostate mpMRI: a comparison with fusion biopsy results.

Authors:  Zeno Falaschi; Stefano Tricca; Silvia Attanasio; Michele Billia; Chiara Airoldi; Ilaria Percivale; Simone Bor; Davide Perri; Alessandro Volpe; Alessandro Carriero
Journal:  Abdom Radiol (NY)       Date:  2022-08-09

5.  The utility of ADC parameters in the diagnosis of clinically significant prostate cancer by 3.0-Tesla diffusion-weighted magnetic resonance imaging.

Authors:  Aylin Altan Kus
Journal:  Pol J Radiol       Date:  2021-05-05

6.  A Pilot Study of Multidimensional Diffusion MRI for Assessment of Tissue Heterogeneity in Prostate Cancer.

Authors:  Björn J Langbein; Filip Szczepankiewicz; Carl-Fredrik Westin; Camden Bay; Stephan E Maier; Adam S Kibel; Clare M Tempany; Fiona M Fennessy
Journal:  Invest Radiol       Date:  2021-12-01       Impact factor: 6.016

7.  Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps.

Authors:  Jinke Xie; Basen Li; Xiangde Min; Peipei Zhang; Chanyuan Fan; Qiubai Li; Liang Wang
Journal:  Front Oncol       Date:  2021-02-04       Impact factor: 6.244

  7 in total

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