OBJECTIVE: To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. MATERIALS AND METHODS: A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. RESULTS: Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). CONCLUSIONS: AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
OBJECTIVE: To investigate the impact of an artificial intelligence (AI) software and quantitative ADC (qADC) on the inter-reader agreement, diagnostic performance, and reporting times of prostate biparametric MRI (bpMRI) for experienced and inexperienced readers. MATERIALS AND METHODS: A total of 170 multiparametric MRI (mpMRI) of patients with suspicion of prostate cancer (PCa) were retrospectively reviewed by one experienced and one inexperienced reader three times, following a wash-out period. First, only the bpMRI sequences, including T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI) sequences, and apparent diffusion coefficient (ADC) maps, were used. Then, bpMRI and quantitative ADC values were used. Lastly, bpMRI and the AI software were used. Inter-reader agreement between the two readers and between each reader and the mpMRI original reports was calculated. Detection rates and reporting times were calculated for each group. RESULTS: Inter-reader agreement with respect to mpMRI was moderate for bpMRI, Quantib, and qADC for both the inexperienced (weighted k of 0.42, 0.45, and 0.41, respectively) and the experienced radiologists (weighted k of 0.44, 0.46, and 0.42, respectively). Detection rate of PCa was similar between the inexperienced (0.24, 0.26, and 0.23) and the experienced reader (0.26, 0.27 and 0.27), for bpMRI, Quantib, and qADC, respectively. Reporting times were lower for Quantib (8.23, 7.11, and 9.87 min for the inexperienced reader and 5.62, 5.07, and 6.21 min for the experienced reader, for bpMRI, Quantib, and qADC, respectively). CONCLUSIONS: AI and qADC did not have a significant impact on the diagnostic performance of both readers. The use of Quantib was associated with lower reporting times.
Authors: Sonia Gaur; Stephanie Harmon; Lauren Rosenblum; Matthew D Greer; Sherif Mehralivand; Mehmet Coskun; Maria J Merino; Bradford J Wood; Joanna H Shih; Peter A Pinto; Peter L Choyke; Baris Turkbey Journal: AJR Am J Roentgenol Date: 2018-05-07 Impact factor: 3.959
Authors: Marloes van der Leest; Erik Cornel; Bas Israël; Rianne Hendriks; Anwar R Padhani; Martijn Hoogenboom; Patrik Zamecnik; Dirk Bakker; Anglita Yanti Setiasti; Jeroen Veltman; Huib van den Hout; Hans van der Lelij; Inge van Oort; Sjoerd Klaver; Frans Debruyne; Michiel Sedelaar; Gerjon Hannink; Maroeska Rovers; Christina Hulsbergen-van de Kaa; Jelle O Barentsz Journal: Eur Urol Date: 2018-11-23 Impact factor: 20.096
Authors: Amirhossein Mohammadian Bajgiran; Sohrab Afshari Mirak; Kyunghyun Sung; Anthony E Sisk; Robert E Reiter; Steven S Raman Journal: AJR Am J Roentgenol Date: 2019-06-19 Impact factor: 3.959
Authors: Baris Turkbey; Andrew B Rosenkrantz; Masoom A Haider; Anwar R Padhani; Geert Villeirs; Katarzyna J Macura; Clare M Tempany; Peter L Choyke; Francois Cornud; Daniel J Margolis; Harriet C Thoeny; Sadhna Verma; Jelle Barentsz; Jeffrey C Weinreb Journal: Eur Urol Date: 2019-03-18 Impact factor: 20.096
Authors: Hashim U Ahmed; Ahmed El-Shater Bosaily; Louise C Brown; Rhian Gabe; Richard Kaplan; Mahesh K Parmar; Yolanda Collaco-Moraes; Katie Ward; Richard G Hindley; Alex Freeman; Alex P Kirkham; Robert Oldroyd; Chris Parker; Mark Emberton Journal: Lancet Date: 2017-01-20 Impact factor: 79.321
Authors: Valentina Brancato; Giuseppe Di Costanzo; Luca Basso; Liberatore Tramontano; Marta Puglia; Alfonso Ragozzino; And Carlo Cavaliere Journal: Diagnostics (Basel) Date: 2020-03-17