Literature DB >> 12732700

Computer-aided diagnosis to distinguish benign from malignant solitary pulmonary nodules on radiographs: ROC analysis of radiologists' performance--initial experience.

Junji Shiraishi1, Hiroyuki Abe, Roger Engelmann, Masahito Aoyama, Heber MacMahon, Kunio Doi.   

Abstract

PURPOSE: To evaluate radiologists' performance for determining a distinction between benign and malignant pulmonary nodules on chest radiographs without and with use of a computer-aided diagnosis scheme.
MATERIALS AND METHODS: Fifty-three chest radiographs that depicted 31 primary lung cancers and 22 benign nodules were used. The likelihood measure of malignancy for each nodule was determined by using an automated computerized scheme. Sixteen radiologists (nine attending radiologists and seven radiology residents) participated in an observer study in which cases were interpreted first without and then with use of the scheme. The radiologists' performance was evaluated with receiver operating characteristic analysis.
RESULTS: The mean area under the best-fit binormal receiver operating characteristic curve plotted in the unit square (Az) values of radiologists who interpreted images without and with the scheme were 0.743 and 0.817, respectively. The performance of radiologists was improved significantly when the scheme was used (P =.002). However, the performance (Az = 0.889) of the computer alone exceeded these results by a substantial margin. The average change in radiologists' confidence level for interpretation without and with the scheme was highly correlated (r = 0.845) with the likelihood measure of malignancy, which was presented as computer output.
CONCLUSION: This scheme for computer-aided diagnosis has the potential to improve the accuracy of radiologists' performance in the classification of benign and malignant solitary pulmonary nodules. Copyright RSNA, 2003

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Mesh:

Year:  2003        PMID: 12732700     DOI: 10.1148/radiol.2272020498

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


  15 in total

Review 1.  [Modern diagnosis of lung nodules].

Authors:  N D Abolmaali; T J Vogl
Journal:  Radiologe       Date:  2004-05       Impact factor: 0.635

Review 2.  Recent progress in computer-aided diagnosis of lung nodules on thin-section CT.

Authors:  Qiang Li
Journal:  Comput Med Imaging Graph       Date:  2007-03-21       Impact factor: 4.790

Review 3.  Computer-aided diagnosis in medical imaging: historical review, current status and future potential.

Authors:  Kunio Doi
Journal:  Comput Med Imaging Graph       Date:  2007-03-08       Impact factor: 4.790

4.  Feature selection and performance evaluation of support vector machine (SVM)-based classifier for differentiating benign and malignant pulmonary nodules by computed tomography.

Authors:  Yanjie Zhu; Yongqiang Tan; Yanqing Hua; Mingpeng Wang; Guozhen Zhang; Jianguo Zhang
Journal:  J Digit Imaging       Date:  2009-02-26       Impact factor: 4.056

5.  Observer study for evaluating potential utility of a super-high-resolution LCD in the detection of clustered microcalcifications on digital mammograms.

Authors:  Junji Shiraishi; Hiroyuki Abe; Katsuhiro Ichikawa; Robert A Schmidt; Kunio Doi
Journal:  J Digit Imaging       Date:  2009-03-10       Impact factor: 4.056

6.  Computer-aided diagnosis of pulmonary nodules on CT scans: improvement of classification performance with nodule surface features.

Authors:  Ted W Way; Berkman Sahiner; Heang-Ping Chan; Lubomir Hadjiiski; Philip N Cascade; Aamer Chughtai; Naama Bogot; Ella Kazerooni
Journal:  Med Phys       Date:  2009-07       Impact factor: 4.071

Review 7.  Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

Authors:  Feng Li
Journal:  Radiol Phys Technol       Date:  2015-05-17

8.  Computer-aided diagnosis for detection of lacunar infarcts on MR images: ROC analysis of radiologists' performance.

Authors:  Yoshikazu Uchiyama; Takahiko Asano; Hiroki Kato; Takeshi Hara; Masayuki Kanematsu; Hiroaki Hoshi; Toru Iwama; Hiroshi Fujita
Journal:  J Digit Imaging       Date:  2012-08       Impact factor: 4.056

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

Authors:  Masami Kawagishi; Bin Chen; Daisuke Furukawa; Hiroyuki Sekiguchi; Koji Sakai; Takeshi Kubo; Masahiro Yakami; Koji Fujimoto; Ryo Sakamoto; Yutaka Emoto; Gakuto Aoyama; Yoshio Iizuka; Keita Nakagomi; Hiroyuki Yamamoto; Kaori Togashi
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-03-11       Impact factor: 2.924

10.  Improved detection of pulmonary nodules on energy-subtracted chest radiographs with a commercial computer-aided diagnosis software: comparison with human observers.

Authors:  Zsolt Szucs-Farkas; Michael A Patak; Seyran Yuksel-Hatz; Thomas Ruder; Peter Vock
Journal:  Eur Radiol       Date:  2009-11-21       Impact factor: 5.315

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