Literature DB >> 34860562

Deep learning-based artificial intelligence applications in prostate MRI: brief summary.

Baris Turkbey1, Masoom A Haider2,3,4.   

Abstract

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

Entities:  

Mesh:

Year:  2021        PMID: 34860562      PMCID: PMC8978238          DOI: 10.1259/bjr.20210563

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


  46 in total

1.  Machine learning for the identification of clinically significant prostate cancer on MRI: a meta-analysis.

Authors:  Renato Cuocolo; Maria Brunella Cipullo; Arnaldo Stanzione; Valeria Romeo; Roberta Green; Valeria Cantoni; Andrea Ponsiglione; Lorenzo Ugga; Massimo Imbriaco
Journal:  Eur Radiol       Date:  2020-06-30       Impact factor: 5.315

2.  A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate: Results of a Multireader, Multicase Study.

Authors:  David J Winkel; Angela Tong; Bin Lou; Ali Kamen; Dorin Comaniciu; Jonathan A Disselhorst; Alejandro Rodríguez-Ruiz; Henkjan Huisman; Dieter Szolar; Ivan Shabunin; Moon Hyung Choi; Pengyi Xing; Tobias Penzkofer; Robert Grimm; Heinrich von Busch; Daniel T Boll
Journal:  Invest Radiol       Date:  2021-03-18       Impact factor: 6.016

3.  Comparison of MR/ultrasound fusion-guided biopsy with ultrasound-guided biopsy for the diagnosis of prostate cancer.

Authors:  M Minhaj Siddiqui; Soroush Rais-Bahrami; Baris Turkbey; Arvin K George; Jason Rothwax; Nabeel Shakir; Chinonyerem Okoro; Dima Raskolnikov; Howard L Parnes; W Marston Linehan; Maria J Merino; Richard M Simon; Peter L Choyke; Bradford J Wood; Peter A Pinto
Journal:  JAMA       Date:  2015-01-27       Impact factor: 56.272

Review 4.  Deep Learning in Neuroradiology.

Authors:  G Zaharchuk; E Gong; M Wintermark; D Rubin; C P Langlotz
Journal:  AJNR Am J Neuroradiol       Date:  2018-02-01       Impact factor: 3.825

5.  Prospective Evaluation of PI-RADS™ Version 2 Using the International Society of Urological Pathology Prostate Cancer Grade Group System.

Authors:  Sherif Mehralivand; Sandra Bednarova; Joanna H Shih; Francesca V Mertan; Sonia Gaur; Maria J Merino; Bradford J Wood; Peter A Pinto; Peter L Choyke; Baris Turkbey
Journal:  J Urol       Date:  2017-03-31       Impact factor: 7.450

6.  Deep learning-accelerated T2-weighted imaging of the prostate: Reduction of acquisition time and improvement of image quality.

Authors:  Sebastian Gassenmaier; Saif Afat; Dominik Nickel; Mahmoud Mostapha; Judith Herrmann; Ahmed E Othman
Journal:  Eur J Radiol       Date:  2021-02-15       Impact factor: 3.528

7.  Comparison of Multiparametric Magnetic Resonance Imaging-Targeted Biopsy With Systematic Transrectal Ultrasonography Biopsy for Biopsy-Naive Men at Risk for Prostate Cancer: A Phase 3 Randomized Clinical Trial.

Authors:  Laurence Klotz; Joseph Chin; Peter C Black; Antonio Finelli; Maurice Anidjar; Franck Bladou; Ashley Mercado; Mark Levental; Sangeet Ghai; Silvia D Chang; Laurent Milot; Chirag Patel; Zahra Kassam; Caroline Moore; Veeru Kasivisvanathan; Andrew Loblaw; Marlene Kebabdjian; Craig C Earle; Greg R Pond; Masoom A Haider
Journal:  JAMA Oncol       Date:  2021-04-01       Impact factor: 31.777

Review 8.  Applications of Artificial Intelligence to Prostate Multiparametric MRI (mpMRI): Current and Emerging Trends.

Authors:  Michelle D Bardis; Roozbeh Houshyar; Peter D Chang; Alexander Ushinsky; Justin Glavis-Bloom; Chantal Chahine; Thanh-Lan Bui; Mark Rupasinghe; Christopher G Filippi; Daniel S Chow
Journal:  Cancers (Basel)       Date:  2020-05-11       Impact factor: 6.639

9.  MRI-Targeted, Systematic, and Combined Biopsy for Prostate Cancer Diagnosis.

Authors:  Michael Ahdoot; Andrew R Wilbur; Sarah E Reese; Amir H Lebastchi; Sherif Mehralivand; Patrick T Gomella; Jonathan Bloom; Sandeep Gurram; Minhaj Siddiqui; Paul Pinsky; Howard Parnes; W Marston Linehan; Maria Merino; Peter L Choyke; Joanna H Shih; Baris Turkbey; Bradford J Wood; Peter A Pinto
Journal:  N Engl J Med       Date:  2020-03-05       Impact factor: 91.245

10.  Prostate Cancer Detection using Deep Convolutional Neural Networks.

Authors:  Sunghwan Yoo; Isha Gujrathi; Masoom A Haider; Farzad Khalvati
Journal:  Sci Rep       Date:  2019-12-20       Impact factor: 4.379

View more
  3 in total

1.  Innovations in prostate cancer: introductory editorial.

Authors:  Jurgen J Fütterer; Chan Kyo Kim; Daniel J Margolis
Journal:  Br J Radiol       Date:  2022-03       Impact factor: 3.039

2.  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

Review 3.  Emerging Applications of Deep Learning in Bone Tumors: Current Advances and Challenges.

Authors:  Xiaowen Zhou; Hua Wang; Chengyao Feng; Ruilin Xu; Yu He; Lan Li; Chao Tu
Journal:  Front Oncol       Date:  2022-07-19       Impact factor: 5.738

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.