Literature DB >> 29361244

Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy.

Au Hoang Dinh1, Christelle Melodelima1, Rémi Souchon1, Paul C Moldovan1, Flavie Bratan1, Gaële Pagnoux1, Florence Mège-Lechevallier1, Alain Ruffion1, Sébastien Crouzet1, Marc Colombel1, Olivier Rouvière1.   

Abstract

Purpose To determine the performance of a computer-aided diagnosis (CAD) system trained at characterizing cancers in the peripheral zone (PZ) with a Gleason score of at least 7 in patients referred for multiparametric magnetic resonance (MR) imaging before prostate biopsy. Materials and Methods Two institutional review board-approved prospective databases of patients who underwent multiparametric MR imaging before prostatectomy (database 1) or systematic and targeted biopsy (database 2) were retrospectively used. All patients gave informed consent for inclusion in the databases. A CAD combining the 10th percentile of the apparent diffusion coefficient and the time to peak of enhancement was trained to detect cancers in the PZ with a Gleason score of at least 7 in 106 patients from database 1. The CAD was tested in 129 different patients from database 2. All targeted lesions were prospectively scored at biopsy by using a five-level Likert score. The CAD scores were retrospectively calculated. Biopsy results were used as the reference standard. Areas under the receiver operating characteristic curves (AUCs) were computed for CAD and Likert scores by using binormal smoothing for per-lesion and per-lobe analyses, and a density function for per-patient analysis. Results The CAD outperformed the Likert score in the overall population and all subgroups, except in the transition zone. The difference was statistically significant for the overall population (AUC, 0.95 [95% confidence interval {CI}: 0.90, 0.98] vs 0.88 [95% CI: 0.68, 0.96]; P = .02) at per-patient analysis, and for less-experienced radiologists (<1 year) at per-lesion (AUC, 0.90 [95% CI: 0.81, 0.95] vs 0.83 [95% CI: 0.73, 0.90]; P = .04) and per-lobe (AUC, 0.92 [95% CI: 0.80, 0.96] vs 0.84 [95% CI: 0.72, 0.91]; P = .04) analysis. Conclusion The CAD outperformed the Likert score prospectively assigned at biopsy in characterizing cancers with a Gleason score of at least 7. © RSNA, 2018 Online supplemental material is available for this article.

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Year:  2018        PMID: 29361244     DOI: 10.1148/radiol.2017171265

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


  8 in total

1.  PROSTATEx Challenges for computerized classification of prostate lesions from multiparametric magnetic resonance images.

Authors:  Samuel G Armato; Henkjan Huisman; Karen Drukker; Lubomir Hadjiiski; Justin S Kirby; Nicholas Petrick; George Redmond; Maryellen L Giger; Kenny Cha; Artem Mamonov; Jayashree Kalpathy-Cramer; Keyvan Farahani
Journal:  J Med Imaging (Bellingham)       Date:  2018-11-10

2.  Diagnosis of transition zone prostate cancer using T2-weighted (T2W) MRI: comparison of subjective features and quantitative shape analysis.

Authors:  Satheesh Krishna; Nicola Schieda; Matthew Df McInnes; Trevor A Flood; Rebecca E Thornhill
Journal:  Eur Radiol       Date:  2018-08-13       Impact factor: 5.315

3.  Advanced ultrasound in the diagnosis of prostate cancer.

Authors:  Jean-Michel Correas; Ethan J Halpern; Richard G Barr; Sangeet Ghai; Jochen Walz; Sylvain Bodard; Charles Dariane; Jean de la Rosette
Journal:  World J Urol       Date:  2020-04-18       Impact factor: 4.226

Review 4.  The current role of prostate multiparametric magnetic resonance imaging.

Authors:  Olivier Rouviere; Paul Cezar Moldovan
Journal:  Asian J Urol       Date:  2018-12-11

5.  Detection of ISUP ≥2 prostate cancers using multiparametric MRI: prospective multicentre assessment of the non-inferiority of an artificial intelligence system as compared to the PI-RADS V.2.1 score (CHANGE study).

Authors:  Olivier Rouvière; Rémi Souchon; Carole Lartizien; Adeline Mansuy; Laurent Magaud; Matthieu Colom; Marine Dubreuil-Chambardel; Sabine Debeer; Tristan Jaouen; Audrey Duran; Pascal Rippert; Benjamin Riche; Caterina Monini; Virginie Vlaeminck-Guillem; Julie Haesebaert; Muriel Rabilloud; Sébastien Crouzet
Journal:  BMJ Open       Date:  2022-02-09       Impact factor: 2.692

Review 6.  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

7.  Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

Authors:  Sonia Gaur; Nathan Lay; Stephanie A Harmon; Sreya Doddakashi; Sherif Mehralivand; Burak Argun; Tristan Barrett; Sandra Bednarova; Rossanno Girometti; Ercan Karaarslan; Ali Riza Kural; Aytekin Oto; Andrei S Purysko; Tatjana Antic; Cristina Magi-Galluzzi; Yesim Saglican; Stefano Sioletic; Anne Y Warren; Leonardo Bittencourt; Jurgen J Fütterer; Rajan T Gupta; Ismail Kabakus; Yan Mee Law; Daniel J Margolis; Haytham Shebel; Antonio C Westphalen; Bradford J Wood; Peter A Pinto; Joanna H Shih; Peter L Choyke; Ronald M Summers; Baris Turkbey
Journal:  Oncotarget       Date:  2018-09-18

Review 8.  Emerging methods for prostate cancer imaging: evaluating cancer structure and metabolic alterations more clearly.

Authors:  Adam Retter; Fiona Gong; Tom Syer; Saurabh Singh; Sola Adeleke; Shonit Punwani
Journal:  Mol Oncol       Date:  2021-08-30       Impact factor: 6.603

  8 in total

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