Literature DB >> 26859255

Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study.

Au Hoang Dinh1, Christelle Melodelima1, Rémi Souchon1, Jérôme Lehaire1, Flavie Bratan1, Florence Mège-Lechevallier1, Alain Ruffion1, Sébastien Crouzet1, Marc Colombel1, Olivier Rouvière1.   

Abstract

Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. (©) RSNA, 2016 Online supplemental material is available for this article.

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Year:  2016        PMID: 26859255     DOI: 10.1148/radiol.2016151406

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  20 in total

1.  T2-weighted signal intensity-selected volumetry for prediction of pathological complete response after preoperative chemoradiotherapy in locally advanced rectal cancer.

Authors:  Sungwon Kim; Kyunghwa Han; Nieun Seo; Hye Jin Kim; Myeong-Jin Kim; Woong Sub Koom; Joong Bae Ahn; Joon Seok Lim
Journal:  Eur Radiol       Date:  2018-06-01       Impact factor: 5.315

Review 2.  Multiparametric MRI for prostate cancer diagnosis: current status and future directions.

Authors:  Armando Stabile; Francesco Giganti; Andrew B Rosenkrantz; Samir S Taneja; Geert Villeirs; Inderbir S Gill; Clare Allen; Mark Emberton; Caroline M Moore; Veeru Kasivisvanathan
Journal:  Nat Rev Urol       Date:  2019-07-17       Impact factor: 14.432

3.  Multisite evaluation of radiomic feature reproducibility and discriminability for identifying peripheral zone prostate tumors on MRI.

Authors:  Prathyush Chirra; Patrick Leo; Michael Yim; B Nicolas Bloch; Ardeshir R Rastinehad; Andrei Purysko; Mark Rosen; Anant Madabhushi; Satish E Viswanath
Journal:  J Med Imaging (Bellingham)       Date:  2019-06-14

4.  Development and validation of a logistic regression model to distinguish transition zone cancers from benign prostatic hyperplasia on multi-parametric prostate MRI.

Authors:  Yuji Iyama; Takeshi Nakaura; Kazuhiro Katahira; Ayumi Iyama; Yasunori Nagayama; Seitaro Oda; Daisuke Utsunomiya; Yasuyuki Yamashita
Journal:  Eur Radiol       Date:  2017-03-13       Impact factor: 5.315

5.  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.

Authors:  Jorge Abreu-Gomez; Daniel Walker; Tareq Alotaibi; Matthew D F McInnes; Trevor A Flood; Nicola Schieda
Journal:  Eur Radiol       Date:  2020-03-24       Impact factor: 5.315

6.  Targeted Biopsy Validation of Peripheral Zone Prostate Cancer Characterization With Magnetic Resonance Fingerprinting and Diffusion Mapping.

Authors:  Ananya Panda; Gregory OʼConnor; Wei Ching Lo; Yun Jiang; Seunghee Margevicius; Mark Schluchter; Lee E Ponsky; Vikas Gulani
Journal:  Invest Radiol       Date:  2019-08       Impact factor: 6.016

7.  T2 mapping for the characterization of prostate lesions.

Authors:  Tobias Hepp; Laura Kalmbach; Manuel Kolb; Petros Martirosian; Tom Hilbert; Wolfgang M Thaiss; Mike Notohamiprodjo; Jens Bedke; Konstantin Nikolaou; Arnulf Stenzl; Stephan Kruck; Sascha Kaufmann
Journal:  World J Urol       Date:  2022-03-31       Impact factor: 3.661

8.  Prediction of prostate cancer grade using fractal analysis of perfusion MRI: retrospective proof-of-principle study.

Authors:  Florian Michallek; Henkjan Huisman; Bernd Hamm; Sefer Elezkurtaj; Andreas Maxeiner; Marc Dewey
Journal:  Eur Radiol       Date:  2021-12-16       Impact factor: 7.034

9.  Differentiation of prostate cancer lesions in the Transition Zone by diffusion-weighted MRI.

Authors:  Jie Bao; Ximing Wang; Chunhong Hu; Jianquan Hou; Fenglin Dong; Lingchuan Guo
Journal:  Eur J Radiol Open       Date:  2017-09-29

Review 10.  Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.

Authors:  Jasper J Twilt; Kicky G van Leeuwen; Henkjan J Huisman; Jurgen J Fütterer; Maarten de Rooij
Journal:  Diagnostics (Basel)       Date:  2021-05-26
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