Literature DB >> 17885070

Computer-aided detection of solid lung nodules on follow-up MDCT screening: evaluation of detection, tracking, and reading time.

Catherine Beigelman-Aubry1, Philippe Raffy, Wenjie Yang, Ronald A Castellino, Philippe A Grenier.   

Abstract

OBJECTIVE: The purpose of this article is to assess detection, tracking, and reading time of solid lung nodules > or = 4 mm on pairs of MDCT chest screening examinations using a computer-aided detection (CAD) system.
MATERIALS AND METHODS: Of 54 pairs of low-dose MDCT chest examinations (1.25-mm collimation), two chest radiologists in consensus established that 25 examinations contained 52 nodules > or = 4 mm. All paired examinations were interpreted on the CAD workstation--first without and then with CAD input--for the detection and tracking of lung nodules. A subset of 33 examination pairs was later read on the clinical workstation used in daily practice, and the results were compared for reading time with those on the CAD workstation.
RESULTS: After CAD input, the sensitivity for nodule detection increased statistically significantly for both readers (9.6% and 23%; p < or = 0.025). One cancer initially missed by one radiologist was correctly identified with CAD input. The overall reading time on the CAD workstation and clinical workstation was comparable for both radiologists. On average, readers spent 4-5 minutes per case to read the paired examinations on the CAD workstation and 6-8 seconds per CAD mark. The CAD system successfully matched 91.3% of nodules detected in both examinations. The overall rate of available CAD growth assessment was 54.9% of all nodule pairs.
CONCLUSION: In the context of temporal comparison of MDCT screening examinations, the sensitivity of radiologists for detecting lung nodules > or = 4 mm increased significantly (p < or = 0.025) with CAD input without compromising reading time.

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Year:  2007        PMID: 17885070     DOI: 10.2214/AJR.07.2302

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  19 in total

1.  Detection of noncalcified pulmonary nodules on low-dose MDCT: comparison of the sensitivity of two CAD systems by using a double reference standard.

Authors:  A R Larici; M Amato; P Ordóñez; F Maggi; L Menchini; A Caulo; L Calandriello; G Vallati; S Giunta; M Crecco; L Bonomo
Journal:  Radiol Med       Date:  2012-02-10       Impact factor: 3.469

2.  Workflow-centred evaluation of an automatic lesion tracking software for chemotherapy monitoring by CT.

Authors:  Jan Hendrik Moltz; Melvin D'Anastasi; Andreas Kiessling; Daniel Pinto dos Santos; Christoph Schülke; Heinz-Otto Peitgen
Journal:  Eur Radiol       Date:  2012-06-29       Impact factor: 5.315

3.  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

Review 4.  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

5.  A cloud-based computer-aided detection system improves identification of lung nodules on computed tomography scans of patients with extra-thoracic malignancies.

Authors:  Lorenzo Vassallo; Alberto Traverso; Michelangelo Agnello; Christian Bracco; Delia Campanella; Gabriele Chiara; Maria Evelina Fantacci; Ernesto Lopez Torres; Antonio Manca; Marco Saletta; Valentina Giannini; Simone Mazzetti; Michele Stasi; Piergiorgio Cerello; Daniele Regge
Journal:  Eur Radiol       Date:  2018-06-15       Impact factor: 5.315

6.  Comparing the performance of trained radiographers against experienced radiologists in the UK lung cancer screening (UKLS) trial.

Authors:  Arjun Nair; Natalie Gartland; Bruce Barton; Diane Jones; Leigh Clements; Nicholas J Screaton; John A Holemans; Stephen W Duffy; John K Field; David R Baldwin; David M Hansell; Anand Devaraj
Journal:  Br J Radiol       Date:  2016-07-27       Impact factor: 3.039

7.  Computer-aided nodule detection and volumetry to reduce variability between radiologists in the interpretation of lung nodules at low-dose screening computed tomography.

Authors:  Kyung Nyeo Jeon; Jin Mo Goo; Chang Hyun Lee; Youkyung Lee; Ji Yung Choo; Nyoung Keun Lee; Mi-Suk Shim; In Sun Lee; Kwang Gi Kim; David S Gierada; Kyongtae T Bae
Journal:  Invest Radiol       Date:  2012-08       Impact factor: 6.016

8.  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 9.  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

10.  Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases.

Authors:  Ryo Sakamoto; Masahiro Yakami; Koji Fujimoto; Keita Nakagomi; Takeshi Kubo; Yutaka Emoto; Thai Akasaka; Gakuto Aoyama; Hiroyuki Yamamoto; Michael I Miller; Susumu Mori; Kaori Togashi
Journal:  Radiology       Date:  2017-07-03       Impact factor: 11.105

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