Literature DB >> 12461245

Lung cancers missed at low-dose helical CT screening in a general population: comparison of clinical, histopathologic, and imaging findings.

Feng Li1, Shusuke Sone, Hiroyuki Abe, Heber MacMahon, Samuel G Armato, Kunio Doi.   

Abstract

PURPOSE: To compare clinical, histopathologic, and imaging features of lung cancers missed at low-radiation-dose helical computed tomography (CT).
MATERIALS AND METHODS: Eighty-three primary lung cancers were found during an annual low-dose CT screening program and confirmed histopathologically at either surgery or biopsy. Thirty-two of these lung cancers were missed on 39 CT scans: on 23 scans owing to detection errors and on 16 owing to interpretation errors. The clinical characteristics, CT features, and histopathologic findings of these missed lung cancers were correlated.
RESULTS: All missed cancers were intrapulmonary, and 28 (88%) were stage IA. All 20 detection errors occurred in cases of adenocarcinoma, 17 (85%) of which were well-differentiated tumors and 11 (55%) of which were in nonsmoking women. The mean size of cancers missed owing to detection error, 9.8 mm, was smaller than that of cancers missed owing to interpretation error, 15.9 mm (P <.001). In the detection error group, the percentages of nodules with ground-glass opacity (91%) or judged to be subtle (91%) were greater than those of nodules in the interpretation error group (38% and 25%, respectively) (P <.001). In the detection error group, 83% (19/23) of cancers were overlapped with, obscured by, or similar in appearance to normal structures such as pulmonary vessels. On 14 of the 16 CT scans with which there were interpretation errors, the CT findings mimicked benign disease, and the patients also had underlying lung disease, such as tuberculosis, emphysema, or lung fibrosis.
CONCLUSION: The lung cancers missed at low-dose CT screening in this series generally were very subtle and appeared as small faint nodules, overlapping normal structures, or opacities in a complex background of other disease.

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Year:  2002        PMID: 12461245     DOI: 10.1148/radiol.2253011375

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


  40 in total

1.  Investigation of optimal viewing size for detecting nodular ground-glass opacity on high-resolution computed tomography with cine-mode display.

Authors:  Michihiro Yamaguchi; Yuichi Bessho; Tatsuro Inoue; Yoshiyuki Asai; Tomoshige Matsumoto; Kenya Murase
Journal:  Radiol Phys Technol       Date:  2010-08-19

2.  Evaluation of a method of computer-aided detection (CAD) of pulmonary nodules with computed tomography.

Authors:  G Foti; N Faccioli; M D'Onofrio; A Contro; T Milazzo; R Pozzi Mucelli
Journal:  Radiol Med       Date:  2010-06-23       Impact factor: 3.469

3.  Spectrum of diagnostic errors in radiology.

Authors:  Antonio Pinto; Luca Brunese
Journal:  World J Radiol       Date:  2010-10-28

Review 4.  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 5.  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

6.  Identification and characterization of focal ground-glass opacity in the lungs by high-resolution CT using thin-section multidetector helical CT: experimental study using a chest CT phantom.

Authors:  Duo Liu; Kazuo Awai; Yoshinori Funama; Seitaro Oda; Takeshi Nakaura; Yumi Yanaga; Masahiro Hatemura; Koichi Kawanaka; Yasuyuki Yamashita
Journal:  Radiat Med       Date:  2008-01-31

Review 7.  CAD (computed-aided detection) and CADx (computer aided diagnosis) systems in identifying and characterising lung nodules on chest CT: overview of research, developments and new prospects.

Authors:  F Fraioli; G Serra; R Passariello
Journal:  Radiol Med       Date:  2010-01-15       Impact factor: 3.469

8.  Machine Learning in Computer-aided Diagnosis of the Thorax and Colon in CT: A Survey.

Authors:  Kenji Suzuki
Journal:  IEICE Trans Inf Syst       Date:  2013-04-01

Review 9.  Cancer Screening in the Elderly: A Review of Breast, Colorectal, Lung, and Prostate Cancer Screening.

Authors:  Ashwin A Kotwal; Mara A Schonberg
Journal:  Cancer J       Date:  2017 Jul/Aug       Impact factor: 3.360

10.  A review of computer-aided diagnosis in thoracic and colonic imaging.

Authors:  Kenji Suzuki
Journal:  Quant Imaging Med Surg       Date:  2012-09
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