Literature DB >> 19760237

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

Justus E Roos1, 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.   

Abstract

OBJECTIVE: The diagnostic performance of radiologists using incremental CAD assistance for lung nodule detection on CT and their temporal variation in performance during CAD evaluation was assessed.
METHODS: CAD was applied to 20 chest multidetector-row computed tomography (MDCT) scans containing 190 non-calcified > or =3-mm nodules. After free search, three radiologists independently evaluated a maximum of up to 50 CAD detections/patient. Multiple free-response ROC curves were generated for free search and successive CAD evaluation, by incrementally adding CAD detections one at a time to the radiologists' performance.
RESULTS: The sensitivity for free search was 53% (range, 44%-59%) at 1.15 false positives (FP)/patient and increased with CAD to 69% (range, 59-82%) at 1.45 FP/patient. CAD evaluation initially resulted in a sharp rise in sensitivity of 14% with a minimal increase in FP over a time period of 100 s, followed by flattening of the sensitivity increase to only 2%. This transition resulted from a greater prevalence of true positive (TP) versus FP detections at early CAD evaluation and not by a temporal change in readers' performance. The time spent for TP (9.5 s +/- 4.5 s) and false negative (FN) (8.4 s +/- 6.7 s) detections was similar; FP decisions took two- to three-times longer (14.4 s +/- 8.7 s) than true negative (TN) decisions (4.7 s +/- 1.3 s).
CONCLUSIONS: When CAD output is ordered by CAD score, an initial period of rapid performance improvement slows significantly over time because of non-uniformity in the distribution of TP CAD output and not to a changing reader performance over time.

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Year:  2009        PMID: 19760237      PMCID: PMC4669889          DOI: 10.1007/s00330-009-1596-y

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  24 in total

1.  Computer-aided detection schemes: the effect of limiting the number of cued regions in each case.

Authors:  Bin Zheng; Joseph K Leader; Gordon Abrams; Betty Shindel; Victor Catullo; Walter F Good; David Gur
Journal:  AJR Am J Roentgenol       Date:  2004-03       Impact factor: 3.959

2.  Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society.

Authors:  Heber MacMahon; John H M Austin; Gordon Gamsu; Christian J Herold; James R Jett; David P Naidich; Edward F Patz; Stephen J Swensen
Journal:  Radiology       Date:  2005-11       Impact factor: 11.105

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.  The Lung Image Database Consortium (LIDC): an evaluation of radiologist variability in the identification of lung nodules on CT scans.

Authors:  Samuel G Armato; Michael F McNitt-Gray; Anthony P Reeves; Charles R Meyer; Geoffrey McLennan; Denise R Aberle; Ella A Kazerooni; Heber MacMahon; Edwin J R van Beek; David Yankelevitz; Eric A Hoffman; Claudia I Henschke; Rachael Y Roberts; Matthew S Brown; Roger M Engelmann; Richard C Pais; Christopher W Piker; David Qing; Masha Kocherginsky; Barbara Y Croft; Laurence P Clarke
Journal:  Acad Radiol       Date:  2007-11       Impact factor: 3.173

5.  Lung nodule CAD software as a second reader: a multicenter study.

Authors:  Charles S White; Robert Pugatch; Thomas Koonce; Steven W Rust; Ekta Dharaiya
Journal:  Acad Radiol       Date:  2008-03       Impact factor: 3.173

6.  Application of the McNemar test to non-independent matched pair data.

Authors:  M Eliasziw; A Donner
Journal:  Stat Med       Date:  1991-12       Impact factor: 2.373

7.  Performance evaluation of a computer-aided detection algorithm for solid pulmonary nodules in low-dose and standard-dose MDCT chest examinations and its influence on radiologists.

Authors:  M Das; G Mühlenbruch; S Heinen; A H Mahnken; M Salganicoff; S Stanzel; R W Günther; J E Wildberger
Journal:  Br J Radiol       Date:  2008-11       Impact factor: 3.039

Review 8.  Computer-aided detection and automated CT volumetry of pulmonary nodules.

Authors:  Katharina Marten; Christoph Engelke
Journal:  Eur Radiol       Date:  2006-09-20       Impact factor: 5.315

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

Authors:  Myrna C B Godoy; Peter L Cooperberg; Zeev V Maizlin; Ren Yuan; Annette McWilliams; Stephen Lam; John R Mayo
Journal:  J Thorac Imaging       Date:  2008-02       Impact factor: 3.000

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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  29 in total

1.  Impact of a computer-aided detection (CAD) system integrated into a picture archiving and communication system (PACS) on reader sensitivity and efficiency for the detection of lung nodules in thoracic CT exams.

Authors:  Luca Bogoni; Jane P Ko; Jeffrey Alpert; Vikram Anand; John Fantauzzi; Charles H Florin; Chi Wan Koo; Derek Mason; William Rom; Maria Shiau; Marcos Salganicoff; David P Naidich
Journal:  J Digit Imaging       Date:  2012-12       Impact factor: 4.056

2.  High performance lung nodule detection schemes in CT using local and global information.

Authors:  Wei Guo; Qiang Li
Journal:  Med Phys       Date:  2012-08       Impact factor: 4.071

3.  Computer aided detection and diagnosis in radiology.

Authors:  E Kotter; M Langer
Journal:  Eur Radiol       Date:  2011-01-11       Impact factor: 5.315

4.  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

5.  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

6.  Noninvasive Computed Tomography-based Risk Stratification of Lung Adenocarcinomas in the National Lung Screening Trial.

Authors:  Fabien Maldonado; Fenghai Duan; Sushravya M Raghunath; Srinivasan Rajagopalan; Ronald A Karwoski; Kavita Garg; Erin Greco; Hrudaya Nath; Richard A Robb; Brian J Bartholmai; Tobias Peikert
Journal:  Am J Respir Crit Care Med       Date:  2015-09-15       Impact factor: 21.405

7.  Significance of pulmonary nodules in patients with colorectal cancer.

Authors:  Fabio Pomerri; Salvatore Pucciarelli; Isacco Maretto; Ernesta Perrone; Giovanna Pintacuda; Sara Lonardi; Donato Nitti; Pier Carlo Muzzio
Journal:  Eur Radiol       Date:  2012-04-01       Impact factor: 5.315

8.  Noninvasive risk stratification of lung adenocarcinoma using quantitative computed tomography.

Authors:  Sushravya Raghunath; Fabien Maldonado; Srinivasan Rajagopalan; Ronald A Karwoski; Zackary S DePew; Brian J Bartholmai; Tobias Peikert; Richard A Robb
Journal:  J Thorac Oncol       Date:  2014-11       Impact factor: 15.609

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

Review 10.  The utilisation of convolutional neural networks in detecting pulmonary nodules: a review.

Authors:  Andrew Murphy; Matthew Skalski; Frank Gaillard
Journal:  Br J Radiol       Date:  2018-06-19       Impact factor: 3.039

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