Literature DB >> 20413773

Screening for lung cancer with digital chest radiography: sensitivity and number of secondary work-up CT examinations.

Bartjan de Hoop1, Cornelia Schaefer-Prokop, Hester A Gietema, Pim A de Jong, Bram van Ginneken, Rob J van Klaveren, Mathias Prokop.   

Abstract

PURPOSE: To estimate the performance of digital chest radiography for detection of lung cancer.
MATERIALS AND METHODS: The study had ethics committee approval, and a nested case-control design was used and included 55 patients with lung cancer detected at computed tomography (CT) and confirmed with histologic examination and a sample of 72 of 4873 control subjects without nodules at CT. All patients underwent direct-detector digital chest radiography in two projections within 2 months of the screening CT. Four radiologists with varying experience identified and localized potential cancers on chest radiographs by using a confidence scale of level 1 (no lesion) to 5 (definite lesion). Localization receiver operating characteristic (ROC) analysis was performed. On the basis of the assumption that suspicious lesions seen at chest radiography would lead to further work-up with CT, the number of work-up CT examinations per detected cancer (CT examinations per cancer) was calculated at various confidence levels for the screening population (cancer rate in study population, 1.3%).
RESULTS: Tumor size ranged from 6.8 to 50.7 mm (median, 11.8 mm). Areas under the localization ROC curve ranged from 0.52 to 0.69. Detection rates substantially varied with the observers' experience and confidence level: At a confidence level of 5, detection rates ranged from 18% at one CT examination per cancer to 53% at 13 CT examinations per cancer. At a confidence level of 2 or higher, detection rates ranged from 94% at 62 CT examinations per cancer to 78% at 44 CT examinations per cancer.
CONCLUSION: A detection rate of 94% for lung tumors with a diameter of 6.8-50.7 mm found at CT screening was achievable with chest radiography only at the expense of a high false-positive rate and an excessive number of work-up CT examinations. Detection performance is strongly observer dependent.

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Year:  2010        PMID: 20413773     DOI: 10.1148/radiol.09091308

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


  14 in total

Review 1.  After Detection: The Improved Accuracy of Lung Cancer Assessment Using Radiologic Computer-aided Diagnosis.

Authors:  Guy J Amir; Harold P Lehmann
Journal:  Acad Radiol       Date:  2015-11-23       Impact factor: 3.173

2.  Hybrid method for the detection of pulmonary nodules using positron emission tomography/computed tomography: a preliminary study.

Authors:  Atsushi Teramoto; Hiroshi Fujita; Katsuaki Takahashi; Osamu Yamamuro; Tsuneo Tamaki; Masami Nishio; Toshiki Kobayashi
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-06-23       Impact factor: 2.924

3.  Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs.

Authors:  Szilárd Vajda; Alexandros Karargyris; Stefan Jaeger; K C Santosh; Sema Candemir; Zhiyun Xue; Sameer Antani; George Thoma
Journal:  J Med Syst       Date:  2018-06-29       Impact factor: 4.460

Review 4.  [Conventional and CT diagnostics of bronchial carcinoma].

Authors:  C Schaefer-Prokop
Journal:  Radiologe       Date:  2010-08       Impact factor: 0.635

5.  [Opinion of the Austrian Society of Radiology and the Austrian Society of Pneumology].

Authors:  Helmut Prosch; Michael Studnicka; Edith Eisenhuber; Horst Olschewski; Elisabeth Stiefsohn; Sylvia Hartl; Christian Herold; Otto Burghuber; Gerhard Mostbeck
Journal:  Wien Klin Wochenschr       Date:  2013-05-15       Impact factor: 1.704

6.  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

7.  Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings.

Authors:  Sohee Park; Sang Min Lee; Kyung Hee Lee; Kyu-Hwan Jung; Woong Bae; Jooae Choe; Joon Beom Seo
Journal:  Eur Radiol       Date:  2019-11-20       Impact factor: 5.315

8.  Early lung cancer detection using the self-evaluation scoring questionnaire and chest digital radiography: a 3-year follow-up study in China.

Authors:  Bojiang Chen; Youjuan Wang; Huibi Cao; Dan Liu; Shangfu Zhang; Jun Gao; Jianqun Yu; Yan Huang; Weimin Li
Journal:  J Digit Imaging       Date:  2013-02       Impact factor: 4.056

9.  Detection of coronary calcifications with dual energy chest X-rays: clinical evaluation.

Authors:  Yingnan Song; Hao Wu; Di Wen; Bo Zhu; Philipp Graner; Leslie Ciancibello; Haran Rajeswaran; Karma Salem; Mehrdad Hajmomenian; Robert C Gilkeson; David L Wilson
Journal:  Int J Cardiovasc Imaging       Date:  2020-10-28       Impact factor: 2.357

10.  COST-RISK-BENEFIT ANALYSIS IN DIAGNOSTIC RADIOLOGY: A THEORETICAL AND ECONOMIC BASIS FOR RADIATION PROTECTION OF THE PATIENT.

Authors:  B Michael Moores
Journal:  Radiat Prot Dosimetry       Date:  2015-12-24       Impact factor: 0.972

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