Literature DB >> 18347512

Detection sensitivity of a commercial lung nodule CAD system in a series of pathologically proven lung cancers.

Myrna C B Godoy1, Peter L Cooperberg, Zeev V Maizlin, Ren Yuan, Annette McWilliams, Stephen Lam, John R Mayo.   

Abstract

PURPOSE: To evaluate the performance of a commercially available computer-aided detection (CAD) system in a series of pathologically proven lung cancers.
MATERIALS AND METHODS: Sixty-nine chest computed tomography (CT) scans obtained in 12 subjects (8 females, 4 males, age 51 to 75 y, mean 63 y) with 15 pathologically proven lung cancers were retrospectively selected from 2156 entry and follow-up CT scans from a lung cancer screening program. CT scans were retrospectively analyzed using a commercially available CAD system for detecting lung nodules.
RESULTS: When first detectable proven lung cancer nodules ranged in maximum diameter from 3 to 38 mm (10.4+/-9.2 mm) with CAD detection sensitivity stratified by size: 0/2 (0%) < or =3 mm, 5/8 (62.5%) 4 to 10 mm, 2/3 (66.7%) 11 to 15 mm, 0/0 16 to 20 mm, 2/2 (100%) >20 mm, and overall sensitivity 9/15 (60%). The sensitivity for all CT scans (first detectable and follow-up), stratified by nodule size as above, was, respectively, 0/2, 18/25, 24/28, 6/9, 5/5, and overall 53/69 (76.8%). Excluding nodules <4 mm and pure ground-glass nodules, the sensitivity for all CT scans by size was 18/24 (75%) 4 to 10 mm, 21/22 (95.4%) 11 to 15 mm, 6/6 (100%) 16 to 20 mm, 5/5 (100%) >20 mm, and overall 50/57 (87.7%). At resection (13) or biopsy (2) nodules were: adenocarcinoma (10), squamous cell carcinoma (3), and small cell carcinoma (2).
CONCLUSIONS: The CAD system showed good sensitivity for solid and semisolid cancers > or =4 mm (sensitivity 87.7%) and excellent for those > or =11 mm (sensitivity >95.4%).

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Mesh:

Year:  2008        PMID: 18347512     DOI: 10.1097/RTI.0b013e3181339edb

Source DB:  PubMed          Journal:  J Thorac Imaging        ISSN: 0883-5993            Impact factor:   3.000


  4 in total

1.  Large scale validation of the M5L lung CAD on heterogeneous CT datasets.

Authors:  E Lopez Torres; E Fiorina; F Pennazio; C Peroni; M Saletta; N Camarlinghi; M E Fantacci; P Cerello
Journal:  Med Phys       Date:  2015-04       Impact factor: 4.071

2.  Computed tomographic characteristics of interval and post screen carcinomas in lung cancer screening.

Authors:  Ernst Th Scholten; Nanda Horeweg; Harry J de Koning; Rozemarijn Vliegenthart; Matthijs Oudkerk; Willem P Th M Mali; Pim A de Jong
Journal:  Eur Radiol       Date:  2014-09-04       Impact factor: 5.315

3.  Computer-aided detection (CAD) of lung nodules in CT scans: radiologist performance and reading time with incremental CAD assistance.

Authors:  Justus E Roos; David Paik; David Olsen; Emily G Liu; Lawrence C Chow; Ann N Leung; Robert Mindelzun; Kingshuk R Choudhury; David P Naidich; Sandy Napel; Geoffrey D Rubin
Journal:  Eur Radiol       Date:  2009-09-16       Impact factor: 5.315

Review 4.  A practical and adaptive approach to lung cancer screening: a review of international evidence and position on CT lung cancer screening in the Singaporean population by the College of Radiologists Singapore.

Authors:  Charlene Jin Yee Liew; Lester Chee Hao Leong; Lynette Li San Teo; Ching Ching Ong; Foong Koon Cheah; Wei Ping Tham; Haja Mohamed Mohideen Salahudeen; Chau Hung Lee; Gregory Jon Leng Kaw; Augustine Kim Huat Tee; Ian Yu Yan Tsou; Kiang Hiong Tay; Raymond Quah; Bien Peng Tan; Hong Chou; Daniel Tan; Angeline Choo Choo Poh; Andrew Gee Seng Tan
Journal:  Singapore Med J       Date:  2019-11       Impact factor: 1.858

  4 in total

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