Literature DB >> 23480965

Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: a comparative study.

Sumiaki Matsumoto1, Yoshiharu Ohno, Takatoshi Aoki, Hitoshi Yamagata, Munenobu Nogami, Keiko Matsumoto, Yoshiko Yamashita, Kazuro Sugimura.   

Abstract

PURPOSE: To compare the reading times and detection performances of radiologists in concurrent-reader and second-reader modes of computer-aided detection (CAD) for lung nodules on multidetector computed tomography (CT).
MATERIALS AND METHODS: Fifty clinical multidetector CT datasets containing nodules up to 20mm in diameter were retrospectively collected. For the detection and rating of non-calcified nodules larger than 4mm in diameter, 6 radiologists (3 experienced radiologists and 3 resident radiologists) independently interpreted these datasets twice, once with concurrent-reader CAD and once with second-reader CAD. The reference standard of nodules in the datasets was determined by the consensus of two experienced chest radiologists. The reading times and detection performances in the two modes of CAD were statistically compared, where jackknife free-response receiver operating characteristic (JAFROC) analysis was used for the comparison of detection performances.
RESULTS: Two hundreds and seven nodules constituted the reference standard. Reading time was significantly shorter in the concurrent-reader mode than in the second-reader mode, with the mean reading time for the 6 radiologists being 132s with concurrent-reader CAD and 210s with second-reader CAD (p<0.01). JAFROC analysis revealed no significant difference between the detection performances in the two modes, with the average figure-of-merit value for the 6 radiologists being 0.70 with concurrent-reader CAD and 0.72 with second-reader CAD (p=0.35).
CONCLUSION: In CAD for lung nodules on multidetector CT, the concurrent-reader mode is more time-efficient than the second-reader mode, and there can be no significant difference between the two modes in terms of detection performance of radiologists.
Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Mesh:

Year:  2013        PMID: 23480965     DOI: 10.1016/j.ejrad.2013.02.005

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  8 in total

1.  A comparison of axial versus coronal image viewing in computer-aided detection of lung nodules on CT.

Authors:  Tae Iwasawa; Sumiaki Matsumoto; Takatoshi Aoki; Fumito Okada; Yoshihiro Nishimura; Hitoshi Yamagata; Yoshiharu Ohno
Journal:  Jpn J Radiol       Date:  2014-12-23       Impact factor: 2.374

2.  A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.

Authors:  Lorenzo Vassallo; Alberto Traverso; Michelangelo Agnello; Christian Bracco; Delia Campanella; Gabriele Chiara; Maria Evelina Fantacci; Ernesto Lopez Torres; Antonio Manca; Marco Saletta; Valentina Giannini; Simone Mazzetti; Michele Stasi; Piergiorgio Cerello; Daniele Regge
Journal:  Eur Radiol       Date:  2018-06-15       Impact factor: 5.315

Review 3.  Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications.

Authors:  Mario Silva; Gianluca Milanese; Valeria Seletti; Alarico Ariani; Nicola Sverzellati
Journal:  Br J Radiol       Date:  2018-01-12       Impact factor: 3.039

Review 4.  Lung nodule and cancer detection in computed tomography screening.

Authors:  Geoffrey D Rubin
Journal:  J Thorac Imaging       Date:  2015-03       Impact factor: 3.000

5.  The impact of trained radiographers as concurrent readers on performance and reading time of experienced radiologists in the UK Lung Cancer Screening (UKLS) trial.

Authors:  Arjun Nair; Nicholas J Screaton; John A Holemans; Diane Jones; Leigh Clements; Bruce Barton; Natalie Gartland; Stephen W Duffy; David R Baldwin; John K Field; David M Hansell; Anand Devaraj
Journal:  Eur Radiol       Date:  2017-06-22       Impact factor: 5.315

6.  Evaluation of a deep learning-based computer-aided detection algorithm on chest radiographs: Case-control study.

Authors:  Soo Yun Choi; Sunggyun Park; Minchul Kim; Jongchan Park; Ye Ra Choi; Kwang Nam Jin
Journal:  Medicine (Baltimore)       Date:  2021-04-23       Impact factor: 1.817

Review 7.  Deep Learning Algorithms for Diagnosis of Lung Cancer: A Systematic Review and Meta-Analysis.

Authors:  Gabriele C Forte; Stephan Altmayer; Ricardo F Silva; Mariana T Stefani; Lucas L Libermann; Cesar C Cavion; Ali Youssef; Reza Forghani; Jeremy King; Tan-Lucien Mohamed; Rubens G F Andrade; Bruno Hochhegger
Journal:  Cancers (Basel)       Date:  2022-08-09       Impact factor: 6.575

8.  CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis.

Authors:  Marcus A Badgeley; Manway Liu; Benjamin S Glicksberg; Mark Shervey; John Zech; Khader Shameer; Joseph Lehar; Eric K Oermann; Michael V McConnell; Thomas M Snyder; Joel T Dudley
Journal:  Bioinformatics       Date:  2019-05-01       Impact factor: 6.931

  8 in total

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