Literature DB >> 30470412

Radiologist performance in the detection of lung cancer using CT.

B Al Mohammad1, S L Hillis2, W Reed3, M Alakhras3, P C Brennan3.   

Abstract

AIM: To measure the level of radiologists' performance in lung cancer detection, and to explore radiologists' performance in cancer specialised and non-specialised centres.
MATERIALS AND METHODS: Thirty radiologists read 60 chest computed tomography (CT) examinations. Thirty cases had surgically or biopsy-proven lung cancer and 30 were cancer-free cases. The cancer cases were validated by four expert radiologists who located the malignant lung nodules. Reader performance was evaluated by calculating sensitivity, location sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC). In addition, sensitivity at fixed specificity (0.794) was computed from each reader's estimated ROC curve.
RESULTS: The radiologists had a mean sensitivity of 0.749, sensitivity at fixed specificity of 0.744, location sensitivity of 0.666, specificity of 0.81 and AUC of 0.846. Radiologists in the specialised and non-specialised cancer centres had the following (specialised, non-specialised) pairs of values: sensitivity=(0.80, 0.719); sensitivity for fixed 0.794 specificity=(0.752, 0.740); location sensitivity=(0.712, 0.637); specificity=(0.794, 0.82) and AUC=(0.846, 0.846).
CONCLUSION: The efficacy of radiologists was comparable to other studies. Furthermore, AUC outcomes were similar for specialised and non-specialised cancer centre radiologists, suggesting they have similar discriminatory ability and that the higher sensitivity and lower specificity for specialised-centre radiologists can be attributed to them being less conservative in interpreting case images.
Copyright © 2018 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Mesh:

Year:  2018        PMID: 30470412     DOI: 10.1016/j.crad.2018.10.008

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  4 in total

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Journal:  Front Oncol       Date:  2022-03-16       Impact factor: 6.244

Review 3.  The augmented radiologist: artificial intelligence in the practice of radiology.

Authors:  Erich Sorantin; Michael G Grasser; Ariane Hemmelmayr; Sebastian Tschauner; Franko Hrzic; Veronika Weiss; Jana Lacekova; Andreas Holzinger
Journal:  Pediatr Radiol       Date:  2021-10-19

4.  Analysis of lncRNA and mRNA Transcriptomes Expression in Thyroid Cancer Tissues Among Patients With Exposure of Medical Occupational Radiation.

Authors:  Feng Shi; Ying Liu; Min Li; Peng Wen; Qiu Qin Qian; Yibin Fan; Ruixue Huang
Journal:  Dose Response       Date:  2019-07-25       Impact factor: 2.658

  4 in total

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