Literature DB >> 26629115

Clinical application of a novel computer-aided detection system based on three-dimensional CT images on pulmonary nodule.

Jian-Ye Zeng1, Hai-Hong Ye2, Shi-Xiong Yang1, Ren-Chao Jin3, Qi-Liang Huang1, Yong-Chu Wei1, Si-Guang Huang1, Bin-Qiang Wang1, Jia-Zhou Ye4, Jian-Ying Qin1.   

Abstract

The aim of this study was to investigate the clinical application effects of a novel computer-aided detection (CAD) system based on three-dimensional computed tomography (CT) images on pulmonary nodule. 98 cases with pulmonary nodule (PN) in our hospital from Jun, 2009 to Jun, 2013 were analysed in this study. All cases underwent PN detection both by the simple spiral CT scan and by the computer-aided system based on 3D CT images, respectively. Postoperative pathological results were considered as the "gold standard", for both two checking methods, the diagnostic accuracies for determining benign and malignant PN were calculated. Under simple spiral CT scan method, 63 cases is malignant, including 50 true positive cases and 13 false positive cases from the "gold standard"; 35 cases is benign, 16 true negative case and 19 false negative cases, the Sensitivity 1 (Se1)=0.725, Specificity1 (Sp1)=0.448, Agreement rate1 (Kappa 1)=0.673, J1 (Youden's index 1)=0.173, LR(+)1=1.616, LR(-)1=0.499. Kappa 1=0.673 between the 0.4 and 0.75, has a moderate consistency. Underwent computer-aided detection (CAD) based on 3D CT method, 67cases is malignant, including 62 true positive cases and 7 false positive cases; 31 cases is benign, 24 true negative case and 7 false negative cases, Sensitivity 2 (Se2)=0.899, Specificity2 (Sp2)=0.828, Agreement rate (Kappa 2)=0.877, J2 (Youden's index 2)=0.727, LR(+)2=5.212, LR(-)2=0.123. Kappa 2=0.877 >0.75, has a good consistency. Computer-aided PN detecting system based on 3D CT images has better clinical application value, and can help doctor carry out early diagnosis of lung disease (such as cancer, etc.) through CT images.

Entities:  

Keywords:  Pulmonary nodule; computer-assisted; diagnosis; three-dimensional image

Year:  2015        PMID: 26629115      PMCID: PMC4659004     

Source DB:  PubMed          Journal:  Int J Clin Exp Med        ISSN: 1940-5901


  18 in total

1.  Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique.

Authors:  Y Lee; T Hara; H Fujita; S Itoh; T Ishigaki
Journal:  IEEE Trans Med Imaging       Date:  2001-07       Impact factor: 10.048

2.  Lung cancer: performance of automated lung nodule detection applied to cancers missed in a CT screening program.

Authors:  Samuel G Armato; Feng Li; Maryellen L Giger; Heber MacMahon; Shusuke Sone; Kunio Doi
Journal:  Radiology       Date:  2002-12       Impact factor: 11.105

3.  A novel computer-aided lung nodule detection system for CT images.

Authors:  Maxine Tan; Rudi Deklerck; Bart Jansen; Michel Bister; Jan Cornelis
Journal:  Med Phys       Date:  2011-10       Impact factor: 4.071

4.  Automated assessment of malignant degree of small peripheral adenocarcinomas using volumetric CT data: correlation with pathologic prognostic factors.

Authors:  Masahiro Yanagawa; Yuko Tanaka; Masahiko Kusumoto; Shunichi Watanabe; Ryosuke Tsuchiya; Osamu Honda; Hiromitsu Sumikawa; Atsuo Inoue; Masayoshi Inoue; Meinoshin Okumura; Noriyuki Tomiyama; Takeshi Johkoh
Journal:  Lung Cancer       Date:  2010-04-14       Impact factor: 5.705

5.  A new computationally efficient CAD system for pulmonary nodule detection in CT imagery.

Authors:  Temesguen Messay; Russell C Hardie; Steven K Rogers
Journal:  Med Image Anal       Date:  2010-02-19       Impact factor: 8.545

6.  Automatic detection of lung nodules in CT datasets based on stable 3D mass-spring models.

Authors:  D Cascio; R Magro; F Fauci; M Iacomi; G Raso
Journal:  Comput Biol Med       Date:  2012-09-26       Impact factor: 4.589

Review 7.  Screening for lung cancer.

Authors:  Helmut Prosch; Cornelia Schaefer-Prokop
Journal:  Curr Opin Oncol       Date:  2014-03       Impact factor: 3.645

8.  Cancer statistics, 2012.

Authors:  Rebecca Siegel; Deepa Naishadham; Ahmedin Jemal
Journal:  CA Cancer J Clin       Date:  2012-01-04       Impact factor: 508.702

Review 9.  Lung cancer screening.

Authors:  U Pastorino
Journal:  Br J Cancer       Date:  2010-04-27       Impact factor: 7.640

10.  Automatic Detection of 2D and 3D Lung Nodules in Chest Spiral CT Scans.

Authors:  Ayman El-Baz; Ahmed Elnakib; Mohamed Abou El-Ghar; Georgy Gimel'farb; Robert Falk; Aly Farag
Journal:  Int J Biomed Imaging       Date:  2013-02-12
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  1 in total

Review 1.  Current Applications and Future Impact of Machine Learning in Radiology.

Authors:  Garry Choy; Omid Khalilzadeh; Mark Michalski; Synho Do; Anthony E Samir; Oleg S Pianykh; J Raymond Geis; Pari V Pandharipande; James A Brink; Keith J Dreyer
Journal:  Radiology       Date:  2018-06-26       Impact factor: 11.105

  1 in total

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