Literature DB >> 19000867

Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy.

Tomohiro Hirose1, Norihisa Nitta, Junji Shiraishi, Yukihiro Nagatani, Masashi Takahashi, Kiyoshi Murata.   

Abstract

RATIONALE AND
OBJECTIVES: The aim of this study was to evaluate the usefulness of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector-row computed tomography (MDCT) in terms of improvement in radiologists' diagnostic accuracy in detecting lung nodules, using jackknife free-response receiver-operating characteristic (JAFROC) analysis.
MATERIALS AND METHODS: Twenty-one patients (6 without and 15 with lung nodules) were selected randomly from 120 consecutive thoracic computed tomographic examinations. The gold standard for the presence or absence of nodules in the observer study was determined by consensus of two radiologists. Six expert radiologists participated in a free-response receiver operating characteristic study for the detection of lung nodules on MDCT, in which cases were interpreted first without and then with the output of CAD software. Radiologists were asked to indicate the locations of lung nodule candidates on the monitor with their confidence ratings for the presence of lung nodules.
RESULTS: The performance of the CAD software indicated that the sensitivity in detecting lung nodules was 71.4%, with 0.95 false-positive results per case. When radiologists used the CAD software, the average sensitivity improved from 39.5% to 81.0%, with an increase in the average number of false-positive results from 0.14 to 0.89 per case. The average figure-of-merit values for the six radiologists were 0.390 without and 0.845 with the output of the CAD software, and there was a statistically significant difference (P < .0001) using the JAFROC analysis.
CONCLUSION: The CAD software for the detection of lung nodules on MDCT has the potential to assist radiologists by increasing their accuracy.

Entities:  

Mesh:

Year:  2008        PMID: 19000867     DOI: 10.1016/j.acra.2008.06.009

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


  9 in total

1.  Assessment of performance and reliability of computer-aided detection scheme using content-based image retrieval approach and limited reference database.

Authors:  Xiao Hui Wang; Sang Cheol Park; Bin Zheng
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

2.  Evaluation of computer-assisted quantification of carotid artery stenosis.

Authors:  Christina Biermann; Ilias Tsiflikas; Christoph Thomas; Bernadette Kasperek; Martin Heuschmid; Claus D Claussen
Journal:  J Digit Imaging       Date:  2012-04       Impact factor: 4.056

3.  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

4.  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

5.  CT temporal subtraction: techniques and clinical applications.

Authors:  Takatoshi Aoki; Tohru Kamiya; Huimin Lu; Takashi Terasawa; Midori Ueno; Yoshiko Hayashida; Seiichi Murakami; Yukunori Korogi
Journal:  Quant Imaging Med Surg       Date:  2021-06

6.  Influence of nodule detection software on radiologists' confidence in identifying pulmonary nodules with computed tomography.

Authors:  Paul J Nietert; James G Ravenel; Katherine K Taylor; Gerard A Silvestri
Journal:  J Thorac Imaging       Date:  2011-02       Impact factor: 3.000

Review 7.  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

8.  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

9.  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
  9 in total

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