Literature DB >> 16467210

Pulmonary nodules: estimation of malignancy at thin-section helical CT--effect of computer-aided diagnosis on performance of radiologists.

Kazuo Awai1, Kohei Murao, Akio Ozawa, Yoshiharu Nakayama, Takeshi Nakaura, Duo Liu, Koichi Kawanaka, Yoshinori Funama, Shoji Morishita, Yasuyuki Yamashita.   

Abstract

PURPOSE: To evaluate the effect of a computer-aided diagnosis (CAD) system on the diagnostic performance of radiologists for the estimation of the malignancy of pulmonary nodules on thin-section helical computed tomographic (CT) scans.
MATERIALS AND METHODS: The institutional review board approved use of the CT database; informed specific study-related consent was waived. The institutional review board approved participation of radiologists; informed consent was obtained from all observers. Thirty-three (18 malignant, 15 benign) pulmonary nodules of less than 3.0 cm in maximal diameter were evaluated. Receiver operating characteristic (ROC) analysis with a continuous rating scale was used to compare observer performance for the estimation of the likelihood of malignancy first without and then with the CAD system. The participants were 10 board-certified radiologists and nine radiology residents.
RESULTS: For all 19 participants, the mean area under the best-fit ROC curve (A(z)) values achieved without and with the CAD system were 0.843 +/- 0.097 (standard deviation) and 0.924 +/- 0.043, respectively. The difference was significant (P = .021). The mean A(z) values achieved without and with the CAD system were 0.910 +/- 0.052 and 0.944 +/- 0.040, respectively, for the 10 board-certified radiologists (P = .190) and 0.768 +/- 0.078 and 0.901 +/- 0.036, respectively, for the nine radiology residents (P = .009).
CONCLUSION: Use of the CAD system significantly (P = .009) improved the diagnostic performance of radiology residents for assessment of the malignancy of pulmonary nodules; however, it did not improve that of board-certified radiologists. (c) RSNA, 2006.

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Year:  2006        PMID: 16467210     DOI: 10.1148/radiol.2383050167

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


  26 in total

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Authors:  Guy J Amir; Harold P Lehmann
Journal:  Acad Radiol       Date:  2015-11-23       Impact factor: 3.173

Review 2.  Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography-a review.

Authors:  Heang-Ping Chan; Lubomir Hadjiiski; Chuan Zhou; Berkman Sahiner
Journal:  Acad Radiol       Date:  2008-05       Impact factor: 3.173

3.  An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT.

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Journal:  J Nucl Cardiol       Date:  2010-03-04       Impact factor: 5.952

4.  Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy.

Authors:  Amirhossein Mozaffary; Tugce Agirlar Trabzonlu; Pamela Lombardi; Adeel R Seyal; Rishi Agrawal; Vahid Yaghmai
Journal:  Emerg Radiol       Date:  2019-07-27

5.  A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists.

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Journal:  Int J Comput Assist Radiol Surg       Date:  2017-03-11       Impact factor: 2.924

Review 6.  Characterization of Pulmonary Nodules Based on Features of Margin Sharpness and Texture.

Authors:  José Raniery Ferreira; Marcelo Costa Oliveira; Paulo Mazzoncini de Azevedo-Marques
Journal:  J Digit Imaging       Date:  2018-08       Impact factor: 4.056

7.  Automatic Categorization and Scoring of Solid, Part-Solid and Non-Solid Pulmonary Nodules in CT Images with Convolutional Neural Network.

Authors:  Xiaoguang Tu; Mei Xie; Jingjing Gao; Zheng Ma; Daiqiang Chen; Qingfeng Wang; Samuel G Finlayson; Yangming Ou; Jie-Zhi Cheng
Journal:  Sci Rep       Date:  2017-09-01       Impact factor: 4.379

8.  Classification of lung nodules based on CT images using squeeze-and-excitation network and aggregated residual transformations.

Authors:  Guobin Zhang; Zhiyong Yang; Li Gong; Shan Jiang; Lu Wang; Hongyun Zhang
Journal:  Radiol Med       Date:  2020-01-08       Impact factor: 3.469

9.  Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening.

Authors:  Ernst Th Scholten; Nanda Horeweg; Harry J de Koning; Rozemarijn Vliegenthart; Matthijs Oudkerk; Willem P Th M Mali; Pim A de Jong
Journal:  Eur Radiol       Date:  2014-09-04       Impact factor: 5.315

10.  Noninvasive risk stratification of lung adenocarcinoma using quantitative computed tomography.

Authors:  Sushravya Raghunath; Fabien Maldonado; Srinivasan Rajagopalan; Ronald A Karwoski; Zackary S DePew; Brian J Bartholmai; Tobias Peikert; Richard A Robb
Journal:  J Thorac Oncol       Date:  2014-11       Impact factor: 15.609

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