Literature DB >> 19418048

Computer-aided detection for the identification of pulmonary nodules in pediatric oncology patients: initial experience.

Emma J Helm1, Cicero T Silva, Heidi C Roberts, David Manson, Mike T M Seed, Joao G Amaral, Paul S Babyn.   

Abstract

BACKGROUND: Computer-aided detection (CAD) has been shown to increase the sensitivity for detection of pulmonary nodules in adults. This study reports initial findings utilizing a CAD system for the detection of pediatric pulmonary nodules.
OBJECTIVE: To assess the performance of CAD and pediatric radiologists in the detection of pediatric pulmonary nodules.
MATERIALS AND METHODS: CT scans from a series of pediatric patients with known primary tumors and lung nodules were analyzed by four radiologists and a commercially available CAD system. IRB approval was obtained. Sensitivities were calculated for detection according to nodule size and location.
RESULTS: In 24 children (age 3-18 years) 173 nodules were identified. Overall the sensitivity of CAD was 34%, but the sensitivity of CAD for detection of nodules 4.0 mm or larger was 80%. Overall radiologist sensitivity ranged from 68% to 79%. There were 0.9 CAD false-positives and 0.3-2.4 radiologist false-positives per study.
CONCLUSION: CAD in our pediatric oncology patients had good sensitivity for detection of lung nodules 4 mm and larger with a low number of false-positives. However, the sensitivity was considerably less for nodules smaller than 4 mm.

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Year:  2009        PMID: 19418048     DOI: 10.1007/s00247-009-1259-9

Source DB:  PubMed          Journal:  Pediatr Radiol        ISSN: 0301-0449


  19 in total

1.  Pulmonary nodules at chest CT: effect of computer-aided diagnosis on radiologists' detection performance.

Authors:  Kazuo Awai; Kohei Murao; Akio Ozawa; Masanori Komi; Haruo Hayakawa; Shinichi Hori; Yasumasa Nishimura
Journal:  Radiology       Date:  2004-02       Impact factor: 11.105

2.  Computer-assisted detection of pulmonary nodules: performance evaluation of an expert knowledge-based detection system in consensus reading with experienced and inexperienced chest radiologists.

Authors:  Katharina Marten; Tobias Seyfarth; Florian Auer; Edzard Wiener; Andreas Grillhösl; Silvia Obenauer; Ernst J Rummeny; Christoph Engelke
Journal:  Eur Radiol       Date:  2004-07-03       Impact factor: 5.315

3.  Small pulmonary nodules: effect of two computer-aided detection systems on radiologist performance.

Authors:  Marco Das; Georg Mühlenbruch; Andreas H Mahnken; Thomas G Flohr; Lutz Gündel; Sven Stanzel; Thomas Kraus; Rolf W Günther; Joachim E Wildberger
Journal:  Radiology       Date:  2006-11       Impact factor: 11.105

4.  Pulmonary metastatic nodules: CT-pathologic correlation.

Authors:  K Murata; M Takahashi; M Mori; N Kawaguchi; A Furukawa; Y Ohnaka; R Itoh; K Kawakami; Y Morioka; R Morita
Journal:  Radiology       Date:  1992-02       Impact factor: 11.105

5.  Pulmonary nodule detection: low-dose versus conventional CT.

Authors:  H Rusinek; D P Naidich; G McGuinness; B S Leitman; D I McCauley; G A Krinsky; K Clayton; H Cohen
Journal:  Radiology       Date:  1998-10       Impact factor: 11.105

6.  Pulmonary metastases: pathological anatomy.

Authors:  K M Müller; M Respondek
Journal:  Lung       Date:  1990       Impact factor: 2.584

7.  Variables affecting pulmonary nodule detection with computed tomography: evaluation with three-dimensional computer simulation.

Authors:  D P Naidich; H Rusinek; G McGuinness; B Leitman; D I McCauley; C I Henschke
Journal:  J Thorac Imaging       Date:  1993       Impact factor: 3.000

8.  Pulmonary metastasis: a pathologic and radiologic study.

Authors:  J Crow; G Slavin; L Kreel
Journal:  Cancer       Date:  1981-06-01       Impact factor: 6.860

9.  Paediatric pulmonary nodules: a comparison of computed tomography, thoracotomy findings and histology.

Authors:  P L Robertson; D W Boldt; J F De Campo
Journal:  Clin Radiol       Date:  1988-11       Impact factor: 2.350

10.  Comparison of sensitivity and reading time for the use of computer-aided detection (CAD) of pulmonary nodules at MDCT as concurrent or second reader.

Authors:  F Beyer; L Zierott; E M Fallenberg; K U Juergens; J Stoeckel; W Heindel; D Wormanns
Journal:  Eur Radiol       Date:  2007-05-22       Impact factor: 5.315

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  7 in total

Review 1.  Newer CT applications and their alternatives: what is appropriate in children?

Authors:  R Paul Guillerman
Journal:  Pediatr Radiol       Date:  2011-08-17

Review 2.  Advanced functional thoracic imaging in children: from basic concepts to clinical applications.

Authors:  Hyun Woo Goo
Journal:  Pediatr Radiol       Date:  2013-02-16

Review 3.  Artificial intelligence applications for pediatric oncology imaging.

Authors:  Heike Daldrup-Link
Journal:  Pediatr Radiol       Date:  2019-10-16

Review 4.  One-stop local and whole-body staging of children with cancer.

Authors:  Heike E Daldrup-Link; Ashok J Theruvath; Lucia Baratto; Kristina Elizabeth Hawk
Journal:  Pediatr Radiol       Date:  2021-04-30

5.  Lung cancer differential diagnosis based on the computer assisted radiology: The state of the art.

Authors:  M V Sprindzuk; V A Kovalev; E V Snezhko; S A Kharuzhyk
Journal:  Pol J Radiol       Date:  2010-01

6.  Frequency and characteristics of pulmonary nodules in children at computed tomography.

Authors:  Atia Samim; Annemieke S Littooij; Marry M van den Heuvel-Eibrink; Frank J Wessels; Rutger A J Nievelstein; Pim A de Jong
Journal:  Pediatr Radiol       Date:  2017-09-04

Review 7.  Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification.

Authors:  Nikos Sourlos; Jingxuan Wang; Yeshaswini Nagaraj; Peter van Ooijen; Rozemarijn Vliegenthart
Journal:  Cancers (Basel)       Date:  2022-08-10       Impact factor: 6.575

  7 in total

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