Literature DB >> 19394873

Commercially available computer-aided detection system for pulmonary nodules on thin-section images using 64 detectors-row CT: preliminary study of 48 cases.

Masahiro Yanagawa1, Osamu Honda, Shigeyuki Yoshida, Yusuke Ono, Atsuo Inoue, Tadahisa Daimon, Hiromitsu Sumikawa, Naoki Mihara, Takeshi Johkoh, Noriyuki Tomiyama, Hironobu Nakamura.   

Abstract

RATIONALE AND
OBJECTIVES: Most studies of computer-aided detection (CAD) for pulmonary nodules have focused on solid nodule detection. The aim of this study was to evaluate the performance of a commercially available CAD system in the detection of pulmonary nodules with or without ground-glass opacity (GGO) using 64-detector-row computed tomography compared to visual interpretation.
MATERIALS AND METHODS: Computed tomographic examinations were performed on 48 patients with existing or suspicious pulmonary nodules on chest radiography. Three radiologists independently reported the location and pattern (GGO, solid, or part solid) of each nodule candidate on computed tomographic scans, assigned each a confidence score, and then analyzed all scans using the CAD system. A reference standard was established by a consensus panel of different radiologists, who found 229 noncalcified nodules with diameters > or = 4 mm. True-positive and false-positive results and confidence levels were used to generate jackknife alternative free-response receiver-operating characteristic plots.
RESULTS: The sensitivity of GGO for 3 radiologists (60%-80%) was significantly higher than that for the CAD system (21%) (McNemar's test, P < .0001). For overall and solid nodules, the figure-of-merit values without and with the CAD system were significantly different (P = .005-.04) on jackknife alternative free-response receiver-operating characteristic analysis. For GGO and part-solid nodules, the figure-of-merit values with the CAD system were greater than those without the CAD system, indicating no significant differences.
CONCLUSION: Radiologists are significantly superior to this CAD system in the detection of GGO, but the CAD system can still play a complementary role in detecting nodules with or without GGO.

Entities:  

Mesh:

Year:  2009        PMID: 19394873     DOI: 10.1016/j.acra.2009.01.030

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

1.  Correlation of free-response and receiver-operating-characteristic area-under-the-curve estimates: results from independently conducted FROC∕ROC studies in mammography.

Authors:  Federica Zanca; Stephen L Hillis; Filip Claus; Chantal Van Ongeval; Valerie Celis; Veerle Provoost; Hong-Jun Yoon; Hilde Bosmans
Journal:  Med Phys       Date:  2012-10       Impact factor: 4.071

2.  Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Fatemeh Homayounieh; Chayanin Nitiwarangkul; Shaunagh McDermott; Brent P Little; Inga T Lennes; Jo-Anne O Shepard; Subba R Digumarthy
Journal:  Quant Imaging Med Surg       Date:  2021-04

Review 3.  A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective.

Authors:  Jin Mo Goo
Journal:  Korean J Radiol       Date:  2011-03-03       Impact factor: 3.500

4.  Adaptive Statistical Iterative Reconstruction-Applied Ultra-Low-Dose CT with Radiography-Comparable Radiation Dose: Usefulness for Lung Nodule Detection.

Authors:  Hyun Jung Yoon; Myung Jin Chung; Hye Sun Hwang; Jung Won Moon; Kyung Soo Lee
Journal:  Korean J Radiol       Date:  2015-08-21       Impact factor: 3.500

5.  [Performance of Deep-learning-based Artificial Intelligence on Detection of Pulmonary Nodules in Chest CT].

Authors:  Xinling Li; Fangfang Guo; Zhen Zhou; Fandong Zhang; Qin Wang; Zhijun Peng; Datong Su; Yaguang Fan; Ying Wang
Journal:  Zhongguo Fei Ai Za Zhi       Date:  2019-06-20

6.  Model-based iterative reconstruction for reduction of radiation dose in abdominopelvic CT: comparison to adaptive statistical iterative reconstruction.

Authors:  Koichiro Yasaka; Masaki Katsura; Masaaki Akahane; Jiro Sato; Izuru Matsuda; Kuni Ohtomo
Journal:  Springerplus       Date:  2013-05-07

7.  Role of Computer Aided Diagnosis (CAD) in the detection of pulmonary nodules on 64 row multi detector computed tomography.

Authors:  K Prakashini; Satish Babu; K V Rajgopal; K Raja Kokila
Journal:  Lung India       Date:  2016 Jul-Aug
  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.