Literature DB >> 31599113

Likert vs PI-RADS v2: a comparison of two radiological scoring systems for detection of clinically significant prostate cancer.

Christopher C Khoo1,2, David Eldred-Evans1,2, Max Peters3, Mariana Bertoncelli Tanaka1,2, Mohamed Noureldin1,2, Saiful Miah1,2, Taimur Shah1,2, Martin J Connor1, Deepika Reddy1, Martin Clark4, Amish Lakhani4, Andrea Rockall4, Feargus Hosking-Jervis1, Emma Cullen1, Manit Arya1,2, David Hrouda2, Hasan Qazi5, Mathias Winkler1,2, Henry Tam4, Hashim U Ahmed1,2.   

Abstract

OBJECTIVE: To compare the clinical validity and utility of Likert assessment and the Prostate Imaging Reporting and Data System (PI-RADS) v2 in the detection of clinically significant and insignificant prostate cancer. PATIENTS AND METHODS: A total of 489 pre-biopsy multiparametric magnetic resonance imaging (mpMRI) scans in consecutive patients were subject to prospective paired reporting using both Likert and PI-RADS v2 by expert uro-radiologists. Patients were offered biopsy for any Likert or PI-RADS score ≥4 or a score of 3 with PSA density ≥0.12 ng/mL/mL. Utility was evaluated in terms of proportion biopsied, and proportion of clinically significant and insignificant cancer detected (both overall and on a 'per score' basis). In those patients biopsied, the overall accuracy of each system was assessed by calculating total and partial area under the receiver-operating characteristic (ROC) curves. The primary threshold of significance was Gleason ≥3 + 4. Secondary thresholds of Gleason ≥4 + 3, Ahmed/UCL1 (Gleason ≥4 + 3 or maximum cancer core length [CCL] ≥6 or total CCL≥6) and Ahmed/UCL2 (Gleason ≥3 + 4 or maximum CCL ≥4 or total CCL ≥6) were also used.
RESULTS: The median (interquartile range [IQR]) age was 66 (60-72) years and the median (IQR) prostate-specific antigen level was 7 (5-10) ng/mL. A similar proportion of men met the biopsy threshold and underwent biopsy in both groups (83.8% [Likert] vs 84.8% [PI-RADS v2]; P = 0.704). The Likert system predicted more clinically significant cancers than PI-RADS across all disease thresholds. Rates of insignificant cancers were comparable in each group. ROC analysis of biopsied patients showed that, although both scoring systems performed well as predictors of significant cancer, Likert scoring was superior to PI-RADS v2, exhibiting higher total and partial areas under the ROC curve.
CONCLUSIONS: Both scoring systems demonstrated good diagnostic performance, with similar rates of decision to biopsy. Overall, Likert was superior by all definitions of clinically significant prostate cancer. It has the advantages of being flexible, intuitive and allowing inclusion of clinical data. However, its use should only be considered once radiologists have developed sufficient experience in reporting prostate mpMRI.
© 2019 The Authors BJU International © 2019 BJU International Published by John Wiley & Sons Ltd.

Entities:  

Keywords:  Likert assessment; PI-RADS; early diagnosis; magnetic resonance imaging; prostate cancer

Mesh:

Year:  2019        PMID: 31599113     DOI: 10.1111/bju.14916

Source DB:  PubMed          Journal:  BJU Int        ISSN: 1464-4096            Impact factor:   5.969


  13 in total

Review 1.  Quality checkpoints in the MRI-directed prostate cancer diagnostic pathway.

Authors:  Tristan Barrett; Maarten de Rooij; Francesco Giganti; Clare Allen; Jelle O Barentsz; Anwar R Padhani
Journal:  Nat Rev Urol       Date:  2022-09-27       Impact factor: 16.430

Review 2.  Advances in the selection of patients with prostate cancer for active surveillance.

Authors:  James L Liu; Hiten D Patel; Nora M Haney; Jonathan I Epstein; Alan W Partin
Journal:  Nat Rev Urol       Date:  2021-02-23       Impact factor: 14.432

3.  Comparison of Likert and PI-RADS version 2 MRI scoring systems for the detection of clinically significant prostate cancer.

Authors:  Jeries P Zawaideh; Evis Sala; Maria Pantelidou; Nadeem Shaida; Brendan Koo; Iztok Caglic; Anne Y Warren; Luca Carmisciano; Kasra Saeb-Parsy; Vincent J Gnanapragasam; Christof Kastner; Tristan Barrett
Journal:  Br J Radiol       Date:  2020-06-11       Impact factor: 3.039

4.  PI-RADS and Likert scales for structured reporting in multiparametric MR imaging of the prostate.

Authors:  Shivang Desai; Daniel N Costa
Journal:  Br J Radiol       Date:  2021-09-29       Impact factor: 3.039

Review 5.  Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer.

Authors:  Niklas Harland; Arnulf Stenzl; Tilman Todenhöfer
Journal:  World J Mens Health       Date:  2020-06-24       Impact factor: 5.400

Review 6.  Magnetic resonance imaging-guided prostate biopsy-A review of literature.

Authors:  Kulthe Ramesh Seetharam Bhat; Srinivas Samavedi; Marcio Covas Moschovas; Fikret Fatih Onol; Shannon Roof; Travis Rogers; Vipul R Patel; Ananthakrishnan Sivaraman
Journal:  Asian J Urol       Date:  2020-07-28

7.  Accuracy of Prostate Magnetic Resonance Imaging: Reader Experience Matters.

Authors:  Hyunseon C Kang; Nahyun Jo; Anas Saeed Bamashmos; Mona Ahmed; Jia Sun; John F Ward; Haesun Choi
Journal:  Eur Urol Open Sci       Date:  2021-03-23

8.  Evaluation of PSA and PSA Density in a Multiparametric Magnetic Resonance Imaging-Directed Diagnostic Pathway for Suspected Prostate Cancer: The INNOVATE Trial.

Authors:  Hayley Pye; Saurabh Singh; Joseph M Norris; Lina M Carmona Echeverria; Vasilis Stavrinides; Alistair Grey; Eoin Dinneen; Elly Pilavachi; Joey Clemente; Susan Heavey; Urszula Stopka-Farooqui; Benjamin S Simpson; Elisenda Bonet-Carne; Dominic Patel; Peter Barker; Keith Burling; Nicola Stevens; Tony Ng; Eleftheria Panagiotaki; David Hawkes; Daniel C Alexander; Manuel Rodriguez-Justo; Aiman Haider; Alex Freeman; Alex Kirkham; David Atkinson; Clare Allen; Greg Shaw; Teresita Beeston; Mrishta Brizmohun Appayya; Arash Latifoltojar; Edward W Johnston; Mark Emberton; Caroline M Moore; Hashim U Ahmed; Shonit Punwani; Hayley C Whitaker
Journal:  Cancers (Basel)       Date:  2021-04-20       Impact factor: 6.575

9.  Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.

Authors:  Oscar J Pellicer-Valero; José L Marenco Jiménez; Victor Gonzalez-Perez; Juan Luis Casanova Ramón-Borja; Isabel Martín García; María Barrios Benito; Paula Pelechano Gómez; José Rubio-Briones; María José Rupérez; José D Martín-Guerrero
Journal:  Sci Rep       Date:  2022-02-22       Impact factor: 4.379

10.  MRI-derived radiomics model for baseline prediction of prostate cancer progression on active surveillance.

Authors:  Nikita Sushentsev; Leonardo Rundo; Oleg Blyuss; Vincent J Gnanapragasam; Evis Sala; Tristan Barrett
Journal:  Sci Rep       Date:  2021-06-21       Impact factor: 4.379

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.