Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods. (June 2020)

• Record Type: Journal Article
• Authors: Wildeboer, Rogier R.; van Sloun, Ruud J.G.; Wijkstra, Hessel; Mischi, Massimo
• Is Part Of: Computer methods and programs in biomedicine. Volume 189(2020)
• Publication Date: 2020-06
• Subjects: Artificial intelligence -- Computer-aided diagnosis -- Computer-aided detection -- Machine learning -- Prostate cancer -- Multiparametric imaging -- Magnetic resonance imaging -- Ultrasound; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105316
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Artificial intelligence can play a major role in the interpretation of multiparametric imaging. Many approaches to computer-aided diagnosis of prostate cancer have been proposed, with a trend towards deep-learning approaches in recent years. There is a variety in methods to communicate the computer-aided diagnostic findings to the operator. Abstract: Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.