Literature DB >> 25505225

The value of ADC, T2 signal intensity, and a combination of both parameters to assess Gleason score and primary Gleason grades in patients with known prostate cancer.

Johannes Nowak1, Uwe Malzahn2, Alexander D J Baur3, Uta Reichelt4, Tobias Franiel5, Bernd Hamm3, Tahir Durmus6.   

Abstract

BACKGROUND: The ability to non-invasively analyze tumor aggressiveness is an important predictor for individual treatment stratification and patient outcome in prostate cancer (PCA).
PURPOSE: To evaluate: (i) whether apparent diffusion coefficient (ADC), the T2 signal intensity (SI), and a combination of both parameters allow for an improved discrimination of Gleason Score (GS) ≥7 (intermediate and high risk) and GS <7 (low risk) in PCA; and (ii) whether ADC may distinguish between 3 + 4 and 4 + 3 PCA (primary Gleason grades [pGG]).
MATERIAL AND METHODS: Prostatectomy specimens of 66 patients (mean age, 63 ± 5.6 years; 104 PCA foci) with a preceding multiparametric 1.5 T endorectal coil magnetic resonance imaging (MRI) were included. ADC (b values = 0, 100, 400, 800 s/mm(2)), standardized T2 (T2s), and the ADC/T2s ratio were tested for correlation with GS applying multivariate analysis. ADC cutoff values were calculated for prediction of GS and pGG, and logarithm of the odds (LOGIT) was used to express the probability for GS and pGG. Diagnostic accuracy was assessed by ROC analysis.
RESULTS: We found an almost linear negative relationship of ADC for GS ≥7 (P = 0.002). The effect of ADC for GS ≥7 (adjusted odds ratio = 0.995) was almost identical for peripheral and transition zone PCA (P = 0.013 and P < 0.001, respectively). ADC showed an AUC of 78.9% for discrimination between GS <7 and GS ≥7. An ADC cutoff of <1.005 × 10(-3 )mm(2)/s indicated a GS ≥7 (90.5% sensitivity, 62.5% specificity). Within the group of GS = 7 PCA, an ADC > 0.762 × 10(-3 )mm(2)/s indicated a pGG of 3 (AUC = 69.6%).
CONCLUSION: T2s and the ADC/T2s ratio do not provide additional information regarding prediction of GS. ADC values have a good discriminatory power to distinguish tumors with GS ≥7 from GS <7 and to predict pGG in GS = 7 PCA. © The Foundation Acta Radiologica 2014.

Entities:  

Keywords:  Gleason score; Prostate; cancer; diffusion-weighted imaging (DWI); magnetic resonance imaging (MRI)

Mesh:

Year:  2014        PMID: 25505225     DOI: 10.1177/0284185114561915

Source DB:  PubMed          Journal:  Acta Radiol        ISSN: 0284-1851            Impact factor:   1.990


  12 in total

1.  Dynamic Contrast Enhanced Study in Multiparametric Examination of the Prostate-Can We Make Better Use of It?

Authors:  Silva Guljaš; Mirta Benšić; Zdravka Krivdić Dupan; Oliver Pavlović; Vinko Krajina; Deni Pavoković; Petra Šmit Takač; Matija Hranić; Tamer Salha
Journal:  Tomography       Date:  2022-06-09

Review 2.  Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications.

Authors:  Lizhi Liu; Zhiqiang Tian; Zhenfeng Zhang; Baowei Fei
Journal:  Acad Radiol       Date:  2016-04-25       Impact factor: 3.173

Review 3.  [Imaging of locally advanced prostate cancer : Importance of ultrasound and especially MRI].

Authors:  O Solyanik; B Schlenker; C Gratzke; B Ertl-Wagner; D A Clevert; C Stief; J Ricke; D Nörenberg
Journal:  Urologe A       Date:  2017-11       Impact factor: 0.639

Review 4.  Prostate cancer risk stratification with magnetic resonance imaging.

Authors:  Ely R Felker; Daniel J Margolis; Nima Nassiri; Leonard S Marks
Journal:  Urol Oncol       Date:  2016-03-31       Impact factor: 3.498

Review 5.  [MR imaging of the prostate].

Authors:  P Asbach; M Haas; B Hamm
Journal:  Radiologe       Date:  2015-12       Impact factor: 0.635

6.  Predicting clinically significant prostate cancer from quantitative image features including compressed sensing radial MRI of prostate perfusion using machine learning: comparison with PI-RADS v2 assessment scores.

Authors:  David Jean Winkel; Hanns-Christian Breit; Bibo Shi; Daniel T Boll; Hans-Helge Seifert; Christian Wetterauer
Journal:  Quant Imaging Med Surg       Date:  2020-04

Review 7.  Multiparametric-MRI in diagnosis of prostate cancer.

Authors:  Sangeet Ghai; Masoom A Haider
Journal:  Indian J Urol       Date:  2015 Jul-Sep

8.  Comparison of optimised endovaginal vs external array coil T2-weighted and diffusion-weighted imaging techniques for detecting suspected early stage (IA/IB1) uterine cervical cancer.

Authors:  Kate Downey; Ayoma D Attygalle; Veronica A Morgan; Sharon L Giles; A MacDonald; M Davis; Thomas E J Ind; John H Shepherd; Nandita M deSouza
Journal:  Eur Radiol       Date:  2015-07-11       Impact factor: 5.315

9.  Role of multi-parametric MRI of the prostate for screening and staging: Experience with over 1500 cases.

Authors:  Geoffrey Gaunay; Vinay Patel; Paras Shah; Daniel Moreira; Simon J Hall; Manish A Vira; Michael Schwartz; Jessica Kreshover; Eran Ben-Levi; Robert Villani; Ardeshir Rastinehad; Lee Richstone
Journal:  Asian J Urol       Date:  2016-11-22

10.  Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

Authors:  Michela Antonelli; Edward W Johnston; Nikolaos Dikaios; King K Cheung; Harbir S Sidhu; Mrishta B Appayya; Francesco Giganti; Lucy A M Simmons; Alex Freeman; Clare Allen; Hashim U Ahmed; David Atkinson; Sebastien Ourselin; Shonit Punwani
Journal:  Eur Radiol       Date:  2019-06-11       Impact factor: 5.315

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