Literature DB >> 29869921

Utility of quantitative apparent diffusion coefficient measurements and normalized apparent diffusion coefficient ratios in the diagnosis of clinically significant peripheral zone prostate cancer.

Tan B Nguyen1, Alexander Ushinsky1, Albert Yang1, Michael Nguyentat1, Sara Fardin1, Edward Uchio1, Chandana Lall1, Thomas Lee1, Roozbeh Houshyar1.   

Abstract

OBJECTIVE: The aim of this study is to evaluate the utility of quantitative apparent diffusion coefficient (ADC) measurements and normalized ADC ratios in multiparametric MRI for the diagnosis of clinically significant peripheral zone (PZ) prostate cancer particularly among equivocally suspicious prostate lesions.
METHODS: A retrospective analysis of 95 patients with PZ lesions by PI-RADSv2 criteria, and who underwent subsequent MRI-US fusion biopsy, was approved by an institutional review board. Two radiologists independently measured ADC values in regions of interest (ROIs) of PZ lesions and calculated normalized ADC ratio based on ROIs in the bladder lumen. Diagnostic performance was evaluated using ROC. Inter observer variability was assessed using intraclass correlation coefficient (ICC).
RESULTS: Mean ADC and normalized ADC ratios for clinically significant and non-clinically significant lesions were 0.763 × 10-3 mm2 s-1, 29.8%; and 1.135 × 10-3 mm2 s-1, 47.2% (p < 0.001), respectively. Area under the ROC curve (AUC) was 0.880 [95% CI (0.816-0.944) and 0.885 (95% CI (0.814-0.955)] for ADC and ADC ratio, respectively. Optimal AUC threshold for ADC was 0.843 × 10-3 mm2 s-1 (Sn 70.5%, Sp 88.2%) and for normalized ADC was 33.1% (Sn 75.0%, Sp 95.7%). intraclass correlation coefficient was high at 0.889.
CONCLUSION: Quantitative ADC measurement in PZ prostate lesions demonstrates excellent diagnostic performance in differentiating clinically significant from non-clinically significant prostate cancer with high inter observer correlation. Advances In knowledge: Quantitative ADC is presented as an additional method to evaluate lesions in mpMRI of the prostate. This technique may be incorporated in new and existing methods to improve detection and discrimination of clinically significant prostate cancer.

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Year:  2018        PMID: 29869921      PMCID: PMC6209473          DOI: 10.1259/bjr.20180091

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  22 in total

1.  Determination of the cutoff level of apparent diffusion coefficient values for detection of prostate cancer.

Authors:  Masako Nagayama; Yuji Watanabe; Akito Terai; Tohru Araki; Kenji Notohara; Akira Okumura; Yoshiki Amoh; Takayoshi Ishimori; Satoru Nakashita; Yoshihiro Dodo
Journal:  Jpn J Radiol       Date:  2011-09-01       Impact factor: 2.374

2.  Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images?

Authors:  Baris Turkbey; Vijay P Shah; Yuxi Pang; Marcelino Bernardo; Sheng Xu; Jochen Kruecker; Julia Locklin; Angelo A Baccala; Ardeshir R Rastinehad; Maria J Merino; Joanna H Shih; Bradford J Wood; Peter A Pinto; Peter L Choyke
Journal:  Radiology       Date:  2010-12-21       Impact factor: 11.105

3.  Assessment of aggressiveness of prostate cancer: correlation of apparent diffusion coefficient with histologic grade after radical prostatectomy.

Authors:  Sadhna Verma; Arumugam Rajesh; Humberto Morales; Lisa Lemen; Gordon Bills; Mark Delworth; Krish Gaitonde; Jun Ying; Ranasinghe Samartunga; Michael Lamba
Journal:  AJR Am J Roentgenol       Date:  2011-02       Impact factor: 3.959

4.  Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer.

Authors:  Thomas Hambrock; Diederik M Somford; Henkjan J Huisman; Inge M van Oort; J Alfred Witjes; Christina A Hulsbergen-van de Kaa; Thomas Scheenen; Jelle O Barentsz
Journal:  Radiology       Date:  2011-05       Impact factor: 11.105

5.  Cancer Statistics, 2017.

Authors:  Rebecca L Siegel; Kimberly D Miller; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2017-01-05       Impact factor: 508.702

Review 6.  Can Clinically Significant Prostate Cancer Be Detected with Multiparametric Magnetic Resonance Imaging? A Systematic Review of the Literature.

Authors:  Jurgen J Fütterer; Alberto Briganti; Pieter De Visschere; Mark Emberton; Gianluca Giannarini; Alex Kirkham; Samir S Taneja; Harriet Thoeny; Geert Villeirs; Arnauld Villers
Journal:  Eur Urol       Date:  2015-02-02       Impact factor: 20.096

Review 7.  Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer.

Authors:  Baris Turkbey; Anna M Brown; Sandeep Sankineni; Bradford J Wood; Peter A Pinto; Peter L Choyke
Journal:  CA Cancer J Clin       Date:  2015-11-23       Impact factor: 508.702

8.  Interpatient variation in normal peripheral zone apparent diffusion coefficient: effect on the prediction of prostate cancer aggressiveness.

Authors:  Geert J S Litjens; Thomas Hambrock; Christina Hulsbergen-van de Kaa; Jelle O Barentsz; Henkjan J Huisman
Journal:  Radiology       Date:  2012-08-24       Impact factor: 11.105

9.  PI-RADS version 2: quantitative analysis aids reliable interpretation of diffusion-weighted imaging for prostate cancer.

Authors:  Sung Yoon Park; Su-Jin Shin; Dae Chul Jung; Nam Hoon Cho; Young Deuk Choi; Koon Ho Rha; Sung Joon Hong; Young Taik Oh
Journal:  Eur Radiol       Date:  2016-12-12       Impact factor: 5.315

10.  Preoperative Evaluation of Prostate Cancer Aggressiveness: Using ADC and ADC Ratio in Determining Gleason Score.

Authors:  Sungmin Woo; Sang Youn Kim; Jeong Yeon Cho; Seung Hyup Kim
Journal:  AJR Am J Roentgenol       Date:  2016-04-14       Impact factor: 3.959

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  5 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.  Accuracy of multiparametric magnetic resonance imaging for diagnosing prostate Cancer: a systematic review and meta-analysis.

Authors:  Liang Zhen; Xiaoqiang Liu; Chen Yegang; Yang Yongjiao; Xu Yawei; Kang Jiaqi; Wang Xianhao; Song Yuxuan; Hu Rui; Zhang Wei; Ou Ningjing
Journal:  BMC Cancer       Date:  2019-12-23       Impact factor: 4.430

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.  Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study.

Authors:  Lei Hu; Da Wei Zhou; Cai Xia Fu; Thomas Benkert; Yun Feng Xiao; Li Ming Wei; Jun Gong Zhao
Journal:  Front Oncol       Date:  2021-09-09       Impact factor: 6.244

Review 5.  Prostate minimally invasive procedures: complications and normal vs. abnormal findings on multiparametric magnetic resonance imaging (mpMRI).

Authors:  Thanh-Lan Bui; Justin Glavis-Bloom; Chantal Chahine; Raj Mehta; Taylor Wolfe; Param Bhatter; Mark Rupasinghe; Joseph Carbone; Masoom A Haider; Francesco Giganti; Simone Giona; Aytekin Oto; Grace Lee; Roozbeh Houshyar
Journal:  Abdom Radiol (NY)       Date:  2021-05-11
  5 in total

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