Literature DB >> 32960726

MRI-based Bosniak Classification of Cystic Renal Masses, Version 2019: Interobserver Agreement, Impact of Readers' Experience, and Diagnostic Performance.

Xu Bai1, Song-Mei Sun1, Wei Xu1, Huan-Huan Kang1, Lin Li1, Ye-Qiang Jin1, Qing-Ge-Le Gong1, Guo-Cheng Liang1, Hong-Yan Liu1, Lin-Lin Liu1, Si-Lu Chen1, Qing-Rong Wang1, Peng Wu1, Ai-Tao Guo1, Qing-Bo Huang1, Xiao-Jing Zhang1, Hui-Yi Ye1, Hai-Yi Wang1.   

Abstract

Background The 2019 Bosniak classification (version 2019) of cystic renal masses (CRMs) provides a systematic update to the currently used 2005 Bosniak classification (version 2005). Further validation is required before widespread application. Purpose To evaluate the interobserver agreement of MRI criteria, the impact of readers' experience, and the diagnostic performance between version 2019 and version 2005. Materials and Methods From January 2009 to December 2018, consecutive patients with CRM who had undergone renal MRI and surgical-pathologic examination were included in this retrospective study. On the basis of version 2019 and version 2005, all CRMs were independently classified by eight radiologists with different levels of experience. By using multirater κ statistics, interobserver agreement was evaluated with comparisons between classifications and between senior and junior radiologists. Diagnostic performance between classifications by dichotomizing classes I-IV into lower (I-IIF) and higher (III-IV) classes was compared by using the McNemar test. P < .05 was considered to indicate a statistically significant difference. Results A total of 207 patients (mean age ± standard deviation, 49 years ± 12; 139 male and 68 female patients) with CRMs were included. Overall, interobserver agreement was higher with version 2019 than version 2005 (weighted κ = 0.64 vs 0.50, respectively; P < .001). Interobserver agreement between senior and junior radiologists did not differ between version 2019 (weighted κ = 0.65 vs 0.64, respectively; P = .71) and version 2005 (weighted κ = 0.54 vs 0.46; P < .001). Diagnostic specificity for malignancy was higher with version 2019 than with version 2005 (83% [92 of 111] vs 68% [75 of 111], respectively; P < .001), without any difference in sensitivity (89% [85 of 96] vs 84% [81 of 96]; P = .34). Conclusion In the updated Bosniak classification, interobserver agreement improved and was unaffected by observers' experience. The diagnostic performance with version 2019 was superior to that with version 2005, with higher specificity. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Choyke in this issue.

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Year:  2020        PMID: 32960726     DOI: 10.1148/radiol.2020200478

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


  9 in total

1.  The Bosniak Classification Gets Even Better.

Authors:  Peter L Choyke
Journal:  Radiology       Date:  2020-09-22       Impact factor: 11.105

2.  Interrater Agreement of Bosniak Classification Version 2019 and Version 2005 for Cystic Renal Masses at CT and MRI.

Authors:  Kimberly L Shampain; Prasad R Shankar; Jonathan P Troost; Maarten L Galantowicz; Rudra A Pampati; Taylor R Schoenheit; David A Shlensky; Daniel Barkmeier; Nicole E Curci; Ravi K Kaza; Shokoufeh Khalatbari; Matthew S Davenport
Journal:  Radiology       Date:  2021-11-02       Impact factor: 11.105

3.  Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort.

Authors:  Paul Schuster; Amandine Crombé; Hubert Nivet; Alice Berger; Laurent Pourriol; Nicolas Favard; Alban Chazot; Florian Alonzo-Lacroix; Emile Youssof; Alexandre Ben Cheikh; Julien Balique; Basile Porta; François Petitpierre; Grégoire Bouquet; Charles Mastier; Flavie Bratan; Jean-François Bergerot; Vivien Thomson; Nathan Banaste; Guillaume Gorincour
Journal:  Sci Rep       Date:  2021-04-26       Impact factor: 4.379

4.  DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

Authors:  Thomas Yi; Ian Pan; Scott Collins; Fiona Chen; Robert Cueto; Ben Hsieh; Celina Hsieh; Jessica L Smith; Li Yang; Wei-Hua Liao; Lisa H Merck; Harrison Bai; Derek Merck
Journal:  J Digit Imaging       Date:  2021-11-02       Impact factor: 4.903

Review 5.  Diagnostic imaging in COVID-19 pneumonia: a literature review.

Authors:  Sarah Campagnano; Flavia Angelini; Giovanni Battista Fonsi; Simone Novelli; Francesco Maria Drudi
Journal:  J Ultrasound       Date:  2021-02-15

6.  A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis.

Authors:  Fatemeh Khatami; Mohammad Saatchi; Seyed Saeed Tamehri Zadeh; Zahra Sadat Aghamir; Alireza Namazi Shabestari; Leonardo Oliveira Reis; Seyed Mohammad Kazem Aghamir
Journal:  Sci Rep       Date:  2020-12-28       Impact factor: 4.379

7.  Diagnostic performance of CT lung severity score and quantitative chest CT for stratification of COVID-19 patients.

Authors:  Damiano Caruso; Marta Zerunian; Michela Polici; Francesco Pucciarelli; Gisella Guido; Tiziano Polidori; Carlotta Rucci; Benedetta Bracci; Giuseppe Tremamunno; Andrea Laghi
Journal:  Radiol Med       Date:  2022-02-14       Impact factor: 3.469

8.  Inter-observer and intra-observer agreement of Bosniak classification of cystic renal masses: Comparison between original version to version 2019 and effect of an online support calculator.

Authors:  Heba Osman; Jin Hui Yan; Jason Chan; Javeria Munir; Sumaya Alrasheed; Satheesh Krishna; Nicola Schieda
Journal:  Can Urol Assoc J       Date:  2021-12       Impact factor: 1.862

9.  Inter-reader agreement of the PI-QUAL score for prostate MRI quality in the NeuroSAFE PROOF trial.

Authors:  Francesco Giganti; Eoin Dinneen; Veeru Kasivisvanathan; Aiman Haider; Alex Freeman; Alex Kirkham; Shonit Punwani; Mark Emberton; Greg Shaw; Caroline M Moore; Clare Allen
Journal:  Eur Radiol       Date:  2021-07-29       Impact factor: 5.315

  9 in total

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