Literature DB >> 22710985

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.

Luca Bogoni1, 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.   

Abstract

The objective of this study is to assess the impact on nodule detection and efficiency using a computer-aided detection (CAD) device seamlessly integrated into a commercially available picture archiving and communication system (PACS). Forty-eight consecutive low-dose thoracic computed tomography studies were retrospectively included from an ongoing multi-institutional screening study. CAD results were sent to PACS as a separate image series for each study. Five fellowship-trained thoracic radiologists interpreted each case first on contiguous 5 mm sections, then evaluated the CAD output series (with CAD marks on corresponding axial sections). The standard of reference was based on three-reader agreement with expert adjudication. The time to interpret CAD marking was automatically recorded. A total of 134 true-positive nodules, measuring 3 mm and larger were included in our study; with 85 ≥ 4 and 50 ≥ 5 mm in size. Readers detection improved significantly in each size category when using CAD, respectively, from 44 to 57 % for ≥3 mm, 48 to 61 % for ≥4 mm, and 44 to 60 % for ≥5 mm. CAD stand-alone sensitivity was 65, 68, and 66 % for nodules ≥3, ≥4, and ≥5 mm, respectively, with CAD significantly increasing the false positives for two readers only. The average time to interpret and annotate a CAD mark was 15.1 s, after localizing it in the original image series. The integration of CAD into PACS increases reader sensitivity with minimal impact on interpretation time and supports such implementation into daily clinical practice.

Mesh:

Year:  2012        PMID: 22710985      PMCID: PMC3491162          DOI: 10.1007/s10278-012-9496-0

Source DB:  PubMed          Journal:  J Digit Imaging        ISSN: 0897-1889            Impact factor:   4.056


  37 in total

1.  Chest CT: automated nodule detection and assessment of change over time--preliminary experience.

Authors:  J P Ko; M Betke
Journal:  Radiology       Date:  2001-01       Impact factor: 11.105

2.  Computerized detection of pulmonary nodules on CT scans.

Authors:  S G Armato; M L Giger; C J Moran; J T Blackburn; K Doi; H MacMahon
Journal:  Radiographics       Date:  1999 Sep-Oct       Impact factor: 5.333

3.  Integration of computer assisted bone age assessment with clinical PACS.

Authors:  Ewa Pietka; Sylwia Pospiech-Kurkowska; Arkadiusz Gertych; Fei Cao
Journal:  Comput Med Imaging Graph       Date:  2003       Impact factor: 4.790

4.  Road maps for advancement of radiologic computer-aided detection in the 21st century.

Authors:  Ronald M Summers
Journal:  Radiology       Date:  2003-10       Impact factor: 11.105

Review 5.  Computer-aided diagnosis: how to move from the laboratory to the clinic.

Authors:  Bram van Ginneken; Cornelia M Schaefer-Prokop; Mathias Prokop
Journal:  Radiology       Date:  2011-12       Impact factor: 11.105

6.  Computer-aided detection of pulmonary nodules: influence of nodule characteristics on detection performance.

Authors:  K Marten; C Engelke; T Seyfarth; A Grillhösl; S Obenauer; E J Rummeny
Journal:  Clin Radiol       Date:  2005-02       Impact factor: 2.350

7.  Robust anisotropic Gaussian fitting for volumetric characterization of pulmonary nodules in multislice CT.

Authors:  Kazunori Okada; Dorin Comaniciu; Arun Krishnan
Journal:  IEEE Trans Med Imaging       Date:  2005-03       Impact factor: 10.048

Review 8.  Computer-aided detection and diagnosis at the start of the third millennium.

Authors:  Bradley J Erickson; Brian Bartholmai
Journal:  J Digit Imaging       Date:  2002-09-26       Impact factor: 4.056

9.  Automated detection of pulmonary nodules on CT images: effect of section thickness and reconstruction interval--initial results.

Authors:  Jin-Sung Kim; Jin-Hwan Kim; Gyuseung Cho; Kyongtae T Bae
Journal:  Radiology       Date:  2005-06-13       Impact factor: 11.105

10.  Computer-aided lung nodule detection in CT: results of large-scale observer test.

Authors:  Matthew S Brown; Jonathan G Goldin; Sarah Rogers; Hyun J Kim; Robert D Suh; Michael F McNitt-Gray; Sumit K Shah; Dao Truong; Kathleen Brown; James W Sayre; David W Gjertson; Poonam Batra; Denise R Aberle
Journal:  Acad Radiol       Date:  2005-06       Impact factor: 3.173

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  13 in total

1.  An Official American Thoracic Society Research Statement: A Research Framework for Pulmonary Nodule Evaluation and Management.

Authors:  Christopher G Slatore; Nanda Horeweg; James R Jett; David E Midthun; Charles A Powell; Renda Soylemez Wiener; Juan P Wisnivesky; Michael K Gould
Journal:  Am J Respir Crit Care Med       Date:  2015-08-15       Impact factor: 21.405

2.  Integration of fully automated computer-aided pulmonary nodule detection into CT pulmonary angiography studies in the emergency department: effect on workflow and diagnostic accuracy.

Authors:  Amirhossein Mozaffary; Tugce Agirlar Trabzonlu; Pamela Lombardi; Adeel R Seyal; Rishi Agrawal; Vahid Yaghmai
Journal:  Emerg Radiol       Date:  2019-07-27

3.  Evidence based imaging strategies for solitary pulmonary nodule.

Authors:  Yi-Xiang J Wang; Jing-Shan Gong; Kenji Suzuki; Sameh K Morcos
Journal:  J Thorac Dis       Date:  2014-07       Impact factor: 2.895

4.  How Many Private Data Are Needed for Deep Learning in Lung Nodule Detection on CT Scans? A Retrospective Multicenter Study.

Authors:  Jeong Woo Son; Ji Young Hong; Yoon Kim; Woo Jin Kim; Dae-Yong Shin; Hyun-Soo Choi; So Hyeon Bak; Kyoung Min Moon
Journal:  Cancers (Basel)       Date:  2022-06-28       Impact factor: 6.575

5.  Artificial intelligence-based vessel suppression for detection of sub-solid nodules in lung cancer screening computed tomography.

Authors:  Ramandeep Singh; Mannudeep K Kalra; Fatemeh Homayounieh; Chayanin Nitiwarangkul; Shaunagh McDermott; Brent P Little; Inga T Lennes; Jo-Anne O Shepard; Subba R Digumarthy
Journal:  Quant Imaging Med Surg       Date:  2021-04

Review 6.  Computer-aided detection system for lung cancer in computed tomography scans: review and future prospects.

Authors:  Macedo Firmino; Antônio H Morais; Roberto M Mendoça; Marcel R Dantas; Helio R Hekis; Ricardo Valentim
Journal:  Biomed Eng Online       Date:  2014-04-08       Impact factor: 2.819

7.  Evaluating the performance of a deep learning-based computer-aided diagnosis (DL-CAD) system for detecting and characterizing lung nodules: Comparison with the performance of double reading by radiologists.

Authors:  Li Li; Zhou Liu; Hua Huang; Meng Lin; Dehong Luo
Journal:  Thorac Cancer       Date:  2018-12-08       Impact factor: 3.500

8.  Osteoporosis detection in panoramic radiographs using a deep convolutional neural network-based computer-assisted diagnosis system: a preliminary study.

Authors:  Jae-Seo Lee; Shyam Adhikari; Liu Liu; Ho-Gul Jeong; Hyongsuk Kim; Suk-Ja Yoon
Journal:  Dentomaxillofac Radiol       Date:  2018-07-13       Impact factor: 2.419

Review 9.  An engineering view on megatrends in radiology: digitization to quantitative tools of medicine.

Authors:  Namkug Kim; Jaesoon Choi; Jaeyoun Yi; Seungwook Choi; Seyoun Park; Yongjun Chang; Joon Beom Seo
Journal:  Korean J Radiol       Date:  2013-02-22       Impact factor: 3.500

Review 10.  Deep Learning in Medical Imaging: General Overview.

Authors:  June-Goo Lee; Sanghoon Jun; Young-Won Cho; Hyunna Lee; Guk Bae Kim; Joon Beom Seo; Namkug Kim
Journal:  Korean J Radiol       Date:  2017-05-19       Impact factor: 3.500

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