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.


Journal

Japanese journal of radiology
ISSN: 1867-108X
Titre abrégé: Jpn J Radiol
Pays: Japan
ID NLM: 101490689

Informations de publication

Date de publication:
Feb 2021
Historique:
received: 30 07 2020
accepted: 10 09 2020
pubmed: 18 9 2020
medline: 1 6 2021
entrez: 17 9 2020
Statut: ppublish

Résumé

To evaluate whether a computer-aided vessel-suppression system improves lung nodule detection in routine clinical settings. 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. 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). Vessel-suppressed CT images helped radiologists to improve the sensitivity of pulmonary nodule detection without compromising the false-positive rate.

Identifiants

pubmed: 32940850
doi: 10.1007/s11604-020-01043-y
pii: 10.1007/s11604-020-01043-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

159-164

Références

The national lung screening trial research team. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365:395–409.
doi: 10.1056/NEJMoa1102873
Hirose T, Nitta N, Shiraishi J, Nagatani Y, Takahashi M, Murata K. Evaluation of computer-aided diagnosis (CAD) software for the detection of lung nodules on multidetector row computed tomography (MDCT): JAFROC study for the improvement in radiologists' diagnostic accuracy. Acad Radiol. 2008;15:1505–12.
doi: 10.1016/j.acra.2008.06.009
Kozuka T, Matsukubo Y, Kadoba T, Oda T, Suzuki A, Hyodo T, et al. Efficiency of a computer-aided diagnosis (CAD) system with deep learning in detection of pulmonary nodules on 1-mm-thick images of computed tomography. Jpn J Radiol. 2020. https://doi.org/10.1007/s11604-020-01009-0 .
doi: 10.1007/s11604-020-01009-0 pubmed: 32592003
Lo SB, Freedman MT, Gillis LB, White CS, Mun SK. Journal club: Computer-aided detection of lung nodules on CT with a computerized pulmonary vessel suppressed function. AJR Am J Roentgenol. 2018;210:480–8.
doi: 10.2214/AJR.17.18718
Wagner AK, Hapich A, Psychogios MN, Teichgraber U, Malich A, Papageorgiou I. Computer-aided detection of pulmonary nodules in computed tomography using ClearReadCT. J Med Syst. 2019;43:58.
doi: 10.1007/s10916-019-1180-1
Matsumoto S, Ohno Y, Aoki T, Yamagata H, Nogami M, Matsumoto K, Yamashita Y, Sugimura K. Computer-aided detection of lung nodules on multidetector CT in concurrent-reader and second-reader modes: a comparative study. Eur J Radiol. 2013;82:1332–7.
doi: 10.1016/j.ejrad.2013.02.005
Foti G, Faccioli N, D'Onofrio M, Contro A, Milazzo T, Pozzi MR. Evaluation of a method of computer-aided detection (CAD) of pulmonary nodules with computed tomography. Radiol Med. 2010;115:950–61.
doi: 10.1007/s11547-010-0556-6
MacMahon H, Austin JH, Gamsu G, Herold CJ, Jett JR, Naidich DP, et al. Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology. 2005;237:395–400.
doi: 10.1148/radiol.2372041887
Fleiss JL, Tytun A, Ury HK. A simple approximation for calculating sample sizes for comparing independent proportions. Biometrics. 1980;36:343–6.
doi: 10.2307/2529990
Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18:500–10.
doi: 10.1038/s41568-018-0016-5
Mayo RC, Leung J. Artificial intelligence and deep learning—radiology’s next frontier? Clin Imaging. 2018;49:87–8.
doi: 10.1016/j.clinimag.2017.11.007
Matsumoto M, Koike S, Kashima S, Awai K. Geographic distribution of CT, MRI and PET devices in Japan: a longitudinal analysis based on national census data. PLoS ONE. 2015;10(5):e0126036.
doi: 10.1371/journal.pone.0126036
Scholten ET, Horeweg N, de Koning HJ, Vliegenthart R, Oudkerk M, Mali WP, et al. Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening. Eur Radiol. 2015;25:81–8.
doi: 10.1007/s00330-014-3394-4
Cai J, Xu D, Liu S, Cham MD. The added value of computer-aided detection of small pulmonary nodules and missed lung cancers. J Thorac Imaging. 2018;33:390–5.
doi: 10.1097/RTI.0000000000000362
Beyer F, Zierott L, Fallenberg EM, Juergens KU, Stoeckel J, Heindel W, et al. Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader. Eur Radiol. 2007;17:2941.
doi: 10.1007/s00330-007-0667-1

Auteurs

Taku Takaishi (T)

Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan. jomo.oze@gmail.com.

Yoshiyuki Ozawa (Y)

Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.

Yuya Bando (Y)

Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.

Akiko Yamamoto (A)

Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.

Sachiko Okochi (S)

Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.

Hirochika Suzuki (H)

Konan Kosei Hospital, Takayacho-Omatsubara 137, Konan, Aichi, Japan.

Yuta Shibamoto (Y)

Department of Radiology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan.

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