Literature DB >> 23059738

The effect of computer-aided detection on radiologist performance in the detection of lung cancers previously missed on a chest radiograph.

Seth Kligerman1, Ling Cai, Charles S White.   

Abstract

PURPOSE: The purpose of the study was to determine whether computer-aided detection (CAD) can improve a radiologist's ability to detect lung cancers previously missed on a chest radiograph (CXR).
MATERIALS AND METHODS: Eighty-one cases of lung cancer previously missed on CXR were collected, along with the CXRs of 215 age-matched and emphysema-matched controls without lung cancer. Tumor subtlety was scored from 1 (very subtle) to 5 (very obvious) by expert thoracic radiologists. All 297 CXRs were processed using a CAD system (OnGuard, Version 5.1 Riverain Medical, Miamisburg, OH) to create a set of 2 images for each patient, 1 with and 1 without CAD. Eleven general radiologists took part in a reader study. Each radiologist viewed the CXR without CAD and then the one with CAD for each patient sequentially. Areas of concern, if present, were marked. The degree of confidence in diagnosis was scored on a scale of 0 (no cancer) to 100 (definite) in succession for CXRs without CAD and then for those with CAD. Localization receiver operating characteristic analysis was used for evaluation of the observers' performance.
RESULTS: Of the 81 cancer cases, OnGuard correctly detected 40 tumors with a sensitivity of 49.4%. In the reader study, there was a significant increase in the area under the localization receiver operating characteristic curve with the aid of OnGuard, which increased from 0.38 to 0.43. Aggregate reader sensitivity improved significantly from 0.44 to 0.5 with the use of OnGuard.
CONCLUSION: The use of OnGuard improves reader accuracy and sensitivity for the detection of lung cancers previously missed on CXR.

Entities:  

Mesh:

Year:  2013        PMID: 23059738     DOI: 10.1097/RTI.0b013e31826c29ec

Source DB:  PubMed          Journal:  J Thorac Imaging        ISSN: 0883-5993            Impact factor:   3.000


  8 in total

1.  New methods for using computer-aided detection information for the detection of lung nodules on chest radiographs.

Authors:  S Schalekamp; B van Ginneken; Bgf Heggelman; M Imhof-Tas; I Somers; M Brink; M Spee; Cm Schaefer-Prokop; N Karssemeijer
Journal:  Br J Radiol       Date:  2014-02-17       Impact factor: 3.039

2.  [Detection of lung nodules. New opportunities in chest radiography].

Authors:  S Pötter-Lang; S Schalekamp; C Schaefer-Prokop; M Uffmann
Journal:  Radiologe       Date:  2014-05       Impact factor: 0.635

Review 3.  Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.

Authors:  Steven Schalekamp; Willemijn M Klein; Kicky G van Leeuwen
Journal:  Pediatr Radiol       Date:  2021-09-01

4.  Computer-aided Detection Fidelity of Pulmonary Nodules in Chest Radiograph.

Authors:  Nikolaos Dellios; Ulf Teichgraeber; Robert Chelaru; Ansgar Malich; Ismini E Papageorgiou
Journal:  J Clin Imaging Sci       Date:  2017-02-20

Review 5.  Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.

Authors:  Eui Jin Hwang; Chang Min Park
Journal:  Korean J Radiol       Date:  2020-05       Impact factor: 3.500

Review 6.  Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules.

Authors:  Yasmeen K Tandon; Brian J Bartholmai; Chi Wan Koo
Journal:  J Thorac Dis       Date:  2020-11       Impact factor: 2.895

7.  Computer-Aided System Application Value for Assessing Hip Development.

Authors:  Yaoxian Jiang; Guangyao Yang; Yuan Liang; Qin Shi; Boqi Cui; Xiaodan Chang; Zhaowen Qiu; Xudong Zhao
Journal:  Front Physiol       Date:  2020-12-01       Impact factor: 4.566

Review 8.  The Added Effect of Artificial Intelligence on Physicians' Performance in Detecting Thoracic Pathologies on CT and Chest X-ray: A Systematic Review.

Authors:  Dana Li; Lea Marie Pehrson; Carsten Ammitzbøl Lauridsen; Lea Tøttrup; Marco Fraccaro; Desmond Elliott; Hubert Dariusz Zając; Sune Darkner; Jonathan Frederik Carlsen; Michael Bachmann Nielsen
Journal:  Diagnostics (Basel)       Date:  2021-11-26
  8 in total

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