Literature DB >> 32940850

Incorporation of a computer-aided vessel-suppression system to detect lung nodules in CT images: effect on sensitivity and reading time in routine clinical settings.

Taku Takaishi1, Yoshiyuki Ozawa2, Yuya Bando3, Akiko Yamamoto3, Sachiko Okochi3, Hirochika Suzuki3, Yuta Shibamoto2.   

Abstract

PURPOSE: To evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings.
MATERIALS AND METHODS: We used computer software that automatically suppresses pulmonary vessels on chest CT while preserving pulmonary nodules. Sixty-one chest CT images were included in our study. Three radiologists independently read either standard CT images alone or both computer-aided CT and standard CT images randomly to detect a pulmonary nodule ≥ 4 mm in diameter. After an interval of at least 15 days to avoid recall bias, the three radiologists interpreted the counterpart images of the same patients. The reference standard was decided by an expert panel. The primary endpoint was sensitivity. The secondary endpoint was interpretation time.
RESULTS: The average sensitivity improved with computer-aided CT (72% for standard CT vs. 84% for computer-aided CT, p = 0.02). There was no difference in the false-positive rate (21% for both standard CT and computer-aided CT, p = 0.98). Although the average reading time was 9.5% longer for computer-aided plus standard CT compared with standard CT alone, the difference was not significant (p = 0.11).
CONCLUSION: Vessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.

Entities:  

Keywords:  Computer software; Lung; Radiologists; Tomography; Workflow

Mesh:

Year:  2020        PMID: 32940850     DOI: 10.1007/s11604-020-01043-y

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN: 1867-1071            Impact factor:   2.374


  1 in total

1.  Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography.

Authors:  Takenori Kozuka; Yuko Matsukubo; Tomoya Kadoba; Teruyoshi Oda; Ayako Suzuki; Tomoko Hyodo; SungWoon Im; Hayato Kaida; Yukinobu Yagyu; Masakatsu Tsurusaki; Mitsuru Matsuki; Kazunari Ishii
Journal:  Jpn J Radiol       Date:  2020-06-26       Impact factor: 2.374

  1 in total

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