Literature DB >> 29336601

JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function.

ShihChung B Lo1,2, Matthew T Freedman1,2, Laura B Gillis3, Charles S White4, Seong K Mun1.   

Abstract

OBJECTIVE: The purpose of this study is to evaluate radiologists' performance in detecting actionable nodules on chest CT when aided by a pulmonary vessel image-suppressed function and a computer-aided detection (CADe) system.
MATERIALS AND METHODS: A novel computerized pulmonary vessel image-suppressed function with a built-in CADe (VIS/CADe) system was developed to assist radiologists in interpreting thoracic CT images. Twelve radiologists participated in a comparative study without and with the VIS/CADe using 324 cases (involving 95 cancers and 83 benign nodules). The ratio of nodule-free cases to cases with nodules was 2:1 in the study. Localization ROC (LROC) methods were used for analysis.
RESULTS: In a stand-alone test, the VIS/CADe system detected 89.5% and 82.0% of malignant nodules and all nodules no smaller than 5 mm, respectively. The false-positive rate per CT study was 0.58. For the reader study, the mean area under the LROC curve (LROCAUC) for the detection of lung cancer significantly increased from 0.633 when unaided by VIS/CADe to 0.773 when aided by VIS/CADe (p < 0.01). For the detection of all clinically actionable nodules, the mean LROC-AUC significantly increased from 0.584 when unaided by VIS/CADe to 0.692 when detection was aided by VIS/CADe (p < 0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p < 0.01); specificity decreased from 89.9% to 84.4% (p < 0.01). Radiologist interpretation time significantly decreased by 26%.
CONCLUSION: The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.

Entities:  

Keywords:  cancer screening; computer-aided detection; lung cancer; lung nodule detection; pulmonary vessel image suppression

Mesh:

Year:  2018        PMID: 29336601     DOI: 10.2214/AJR.17.18718

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


  11 in total

1.  Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Using ClearReadCT.

Authors:  Anne-Kathrin Wagner; Arno Hapich; Marios Nikos Psychogios; Ulf Teichgräber; Ansgar Malich; Ismini Papageorgiou
Journal:  J Med Syst       Date:  2019-01-31       Impact factor: 4.460

Review 2.  Machine Learning and Deep Neural Networks in Thoracic and Cardiovascular Imaging.

Authors:  Tara A Retson; Alexandra H Besser; Sean Sall; Daniel Golden; Albert Hsiao
Journal:  J Thorac Imaging       Date:  2019-05       Impact factor: 3.000

Review 3.  Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective.

Authors:  Steven Schalekamp; Willemijn M Klein; Kicky G van Leeuwen
Journal:  Pediatr Radiol       Date:  2021-09-01

4.  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

5.  Content-Based Image Retrieval of Chest CT with Convolutional Neural Network for Diffuse Interstitial Lung Disease: Performance Assessment in Three Major Idiopathic Interstitial Pneumonias.

Authors:  Hye Jeon Hwang; Joon Beom Seo; Sang Min Lee; Eun Young Kim; Beomhee Park; Hyun Jin Bae; Namkug Kim
Journal:  Korean J Radiol       Date:  2020-10-21       Impact factor: 3.500

Review 6.  Artificial Intelligence for the Future Radiology Diagnostic Service.

Authors:  Seong K Mun; Kenneth H Wong; Shih-Chung B Lo; Yanni Li; Shijir Bayarsaikhan
Journal:  Front Mol Biosci       Date:  2021-01-28

Review 7.  Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening.

Authors:  Spencer C Dyer; Brian J Bartholmai; Chi Wan Koo
Journal:  J Thorac Dis       Date:  2020-11       Impact factor: 2.895

Review 8.  The application of artificial intelligence in lung cancer: a narrative review.

Authors:  Huixian Zhang; Die Meng; Siqi Cai; Haoyue Guo; Peixin Chen; Zixuan Zheng; Jun Zhu; Wencheng Zhao; Hao Wang; Sha Zhao; Jia Yu; Yayi He
Journal:  Transl Cancer Res       Date:  2021-05       Impact factor: 1.241

9.  [What can machines do?]

Authors:  Alexander Piotrowski; Fabian Siegel
Journal:  J Urol Urogynakologie       Date:  2021-10-29

Review 10.  Application of Artificial Intelligence in Lung Cancer.

Authors:  Hwa-Yen Chiu; Heng-Sheng Chao; Yuh-Min Chen
Journal:  Cancers (Basel)       Date:  2022-03-08       Impact factor: 6.639

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