Literature DB >> 15816616

A computer-aided diagnosis (CAD) system in lung cancer screening with computed tomography.

Yoshiyuki Abe1, Kouzo Hanai, Makiko Nakano, Yasuyuki Ohkubo, Toshinori Hasizume, Toru Kakizaki, Masato Nakamura, Noboru Niki, Kenji Eguchi, Tadahiko Fujino, Noriyuki Moriyama.   

Abstract

We evaluated a computer-aided diagnosis (CAD) system with automatic detection of pulmonary nodules for lung cancer screening with computed tomography (CT). Five hundred and eighteen participants were examined with low-dose helical CT during a lung cancer screening by three respiratory physicians according to the General Rule edited by the Japan Lung Cancer Society. Four cases were detected by CAD and pathologically diagnosed as lung cancer. We compared the detection capability of the physician and CAD in 301 participants. Three physicians determined 75/301 (24.9%) participants as "e" (suspicious of lung cancer) in consensus without CAD, while 3 participants were added to "e" with CAD. Three physicians did not independently judge as "e" in 14 (18.7%), 16 (21.3%) and 16 (21.3%) out of 75 participants. CAD could not identify 17 (22.7%) nodules of 75 participants, and all 17 were less than 6 mm in diameter. The CAD system offers a useful second opinion when physicians examine patients at lung cancer CT screenings.

Entities:  

Mesh:

Year:  2005        PMID: 15816616

Source DB:  PubMed          Journal:  Anticancer Res        ISSN: 0250-7005            Impact factor:   2.480


  9 in total

1.  Computer-aided diagnosis of malignant or benign thyroid nodes based on ultrasound images.

Authors:  Qin Yu; Tao Jiang; Aiyun Zhou; Lili Zhang; Cheng Zhang; Pan Xu
Journal:  Eur Arch Otorhinolaryngol       Date:  2017-04-07       Impact factor: 2.503

2.  A case of early stage lung cancer detected by repeated cancer screening with positron emission tomography.

Authors:  Ikuko Sakata; Yuichi Ozeki; Katsumi Tamura; Jiro Ishida; Shinsuke Aida; Yoshiyuki Abe
Journal:  Oncol Lett       Date:  2011-11-22       Impact factor: 2.967

3.  Variability in interpretation of low-dose chest CT using computerized assessment in a nationwide lung cancer screening program: comparison of prospective reading at individual institutions and retrospective central reading.

Authors:  Eui Jin Hwang; Jin Mo Goo; Hyae Young Kim; Soon Ho Yoon; Gong Yong Jin; Jaeyoun Yi; Yeol Kim
Journal:  Eur Radiol       Date:  2020-10-30       Impact factor: 5.315

4.  Implementation of the cloud-based computerized interpretation system in a nationwide lung cancer screening with low-dose CT: comparison with the conventional reading system.

Authors:  Eui Jin Hwang; Jin Mo Goo; Hyae Young Kim; Jaeyoun Yi; Soon Ho Yoon; Yeol Kim
Journal:  Eur Radiol       Date:  2020-08-14       Impact factor: 5.315

5.  Lung cancer differential diagnosis based on the computer assisted radiology: The state of the art.

Authors:  M V Sprindzuk; V A Kovalev; E V Snezhko; S A Kharuzhyk
Journal:  Pol J Radiol       Date:  2010-01

6.  Prediction of lung tumor types based on protein attributes by machine learning algorithms.

Authors:  Faezeh Hosseinzadeh; Amir Hossein Kayvanjoo; Mansuor Ebrahimi; Bahram Goliaei
Journal:  Springerplus       Date:  2013-05-24

Review 7.  Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures.

Authors:  Ruben T H M Larue; Gilles Defraene; Dirk De Ruysscher; Philippe Lambin; Wouter van Elmpt
Journal:  Br J Radiol       Date:  2016-12-12       Impact factor: 3.039

8.  Classification of lung nodules in CT scans using three-dimensional deep convolutional neural networks with a checkpoint ensemble method.

Authors:  Hwejin Jung; Bumsoo Kim; Inyeop Lee; Junhyun Lee; Jaewoo Kang
Journal:  BMC Med Imaging       Date:  2018-12-03       Impact factor: 1.930

9.  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
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.