Literature DB >> 18374715

Features of non-small cell lung carcinomas overlooked at digital chest radiography.

M-H Wu1, M B Gotway, T J Lee, M-S Chern, H-C Cheng, J S-C Ko, M-H Sheu, C-Y Chang.   

Abstract

PURPOSE: To examine the imaging features of non-small cell lung carcinomas (NSCLC) overlooked at digital chest radiography (dCXR), and compare general and thoracic radiologists' performance for lung carcinoma detection at dCXR.
METHODS: Frontal and lateral dCXR from 30 consecutive patients with lung carcinoma overlooked during initial interpretation and 30 normal controls were independently retrospectively reviewed by two blinded thoracic radiologists and, in a separate review, three blinded general radiologists. The location, size, histopathology, borders, presence of superimposed structures, and lesion opacity were recorded. Interobserver agreement was calculated, and the detection performance between thoracic and general radiologists was compared.
RESULTS: The average patient age was 67.9 years (range 47-82 years). The average size of carcinomas missed by the thoracic radiologists was 18.1mm (range 10-32 mm). Lesion margins were circumscribed in 29% (2/7), and 71% (5/7) of missed lesions were obscured by anatomical superimposition. Seventy-one percent (5/7) of missed lesions were solid nodules on computed tomography (CT) images. Forty-three percent of lesions were located in the upper lobes and 63% were adenocarcinomas. Compared with general radiologists, the seven NSCLC missed by the thoracic radiologists tended to be smaller (p=0.063), had significantly lower CT density measurements (-92.4+/-87.5 HU versus -70+/-87.2 HU, p=0.050), and more commonly had an ill-defined margin (p=0.026). The clinical stage of the overlooked lesions did not differ between the two groups (p=0.480).
CONCLUSIONS: The lesion size, location, conspicuity, and histopathology impact the likelihood of lung carcinoma detection at dCXR in a fashion similar to that of conventional film-screen techniques.

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Year:  2008        PMID: 18374715     DOI: 10.1016/j.crad.2007.09.011

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


  5 in total

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Journal:  Diagn Interv Radiol       Date:  2017 Mar-Apr       Impact factor: 2.630

2.  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

3.  Sensitivity of chest X-ray for detecting lung cancer in people presenting with symptoms: a systematic review.

Authors:  Stephen H Bradley; Sarah Abraham; Matthew Ej Callister; Adam Grice; William T Hamilton; Rocio Rodriguez Lopez; Bethany Shinkins; Richard D Neal
Journal:  Br J Gen Pract       Date:  2019-11-28       Impact factor: 5.386

4.  Observer training for computer-aided detection of pulmonary nodules in chest radiography.

Authors:  Diederick W De Boo; François van Hoorn; Joost van Schuppen; Laura Schijf; Maeke J Scheerder; Nicole J Freling; Onno Mets; Michael Weber; Cornelia M Schaefer-Prokop
Journal:  Eur Radiol       Date:  2012-03-25       Impact factor: 5.315

5.  Consensus study on the health system and patient-related barriers for lung cancer management in South Africa.

Authors:  Witness Mapanga; Shane A Norris; Wenlong C Chen; Charmaine Blanchard; Anita Graham; Laurel Baldwin-Ragaven; Tom Boyles; Bernard Donde; Linda Greef; Ken Huddle; Busisiwe Khumalo; Elizabeth Leepile; Buhle Lubuzo; Raynolda Makhutle; Yusuf Mayet; Merika Tsitsi; Preethi Mistri; Keletso Mmoledi; Mpho Ratshikana-Moloko; Rajen Morer; Lauren Pretorius; Jayshina Punwasi; Guy A Richards; Paul Ruff; Dineo Shabalala; Maureen Sibadela; Nita Soma; Michelle Wong; Maureen Joffe
Journal:  PLoS One       Date:  2021-02-11       Impact factor: 3.240

  5 in total

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