Literature DB >> 27019363

Low-Dose CT Screening for Lung Cancer: Computer-aided Detection of Missed Lung Cancers.

Mingzhu Liang1, Wei Tang1, Dong Ming Xu1, Artit C Jirapatnakul1, Anthony P Reeves1, Claudia I Henschke1, David Yankelevitz1.   

Abstract

Purpose To update information regarding the usefulness of computer-aided detection (CAD) systems with a focus on the most critical category, that of missed cancers at earlier imaging, for cancers that manifest as a solid nodule. Materials and Methods By using a HIPAA-compliant institutional review board-approved protocol where informed consent was obtained, 50 lung cancers that manifested as a solid nodule on computed tomographic (CT) scans in annual rounds of screening (time 1) were retrospectively identified that could, in retrospect, be identified on the previous CT scans (time 0). Four CAD systems were compared, which were referred to as CAD 1, CAD 2, CAD 3, and CAD 4. The total number of accepted CAD-system-detected nodules at time 0 was determined by consensus of two radiologists and the number of CAD-system-detected nodules that were rejected by the radiologists was also documented. Results At time 0 when all the cancers had been missed, CAD system detection rates for the cancers were 56%, 70%, 68%, and 60% (κ = 0.45) for CAD systems 1, 2, 3, and 4, respectively. At time 1, the rates were 74%, 82%, 82%, and 78% (κ = 0.32), respectively. The average diameter of the 50 cancers at time 0 and time 1 was 4.8 mm and 11.4 mm, respectively. The number of CAD-system-detected nodules that were rejected per CT scan for CAD systems 1-4 at time 0 was 7.4, 1.7, 0.6, and 4.5 respectively. Conclusion CAD systems detected up to 70% of lung cancers that were not detected by the radiologist but failed to detect about 20% of the lung cancers when they were identified by the radiologist, which suggests that CAD may be useful in the role of second reader. (©) RSNA, 2016.

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Year:  2016        PMID: 27019363     DOI: 10.1148/radiol.2016150063

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  28 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.  NCTN Assessment on Current Applications of Radiomics in Oncology.

Authors:  Ke Nie; Hania Al-Hallaq; X Allen Li; Stanley H Benedict; Jason W Sohn; Jean M Moran; Yong Fan; Mi Huang; Michael V Knopp; Jeff M Michalski; James Monroe; Ceferino Obcemea; Christina I Tsien; Timothy Solberg; Jackie Wu; Ping Xia; Ying Xiao; Issam El Naqa
Journal:  Int J Radiat Oncol Biol Phys       Date:  2019-01-31       Impact factor: 7.038

Review 3.  Lung cancer screening: nodule identification and characterization.

Authors:  Ioannis Vlahos; Konstantinos Stefanidis; Sarah Sheard; Arjun Nair; Charles Sayer; Joanne Moser
Journal:  Transl Lung Cancer Res       Date:  2018-06

Review 4.  Lung cancer LDCT screening and mortality reduction - evidence, pitfalls and future perspectives.

Authors:  Matthijs Oudkerk; ShiYuan Liu; Marjolein A Heuvelmans; Joan E Walter; John K Field
Journal:  Nat Rev Clin Oncol       Date:  2020-10-12       Impact factor: 66.675

5.  With or without human interference for precise age estimation based on machine learning?

Authors:  Mengqi Han; Shaoyi Du; Yuyan Ge; Dong Zhang; Yuting Chi; Hong Long; Jing Yang; Yang Yang; Jingmin Xin; Teng Chen; Nanning Zheng; Yu-Cheng Guo
Journal:  Int J Legal Med       Date:  2022-02-14       Impact factor: 2.686

6.  Evaluating a Fully Automated Pulmonary Nodule Detection Approach and Its Impact on Radiologist Performance.

Authors:  Kai Liu; Qiong Li; Jiechao Ma; Zijian Zhou; Mengmeng Sun; Yufeng Deng; Wenting Tu; Yun Wang; Li Fan; Chen Xia; Yi Xiao; Rongguo Zhang; Shiyuan Liu
Journal:  Radiol Artif Intell       Date:  2019-05-29

7.  Performance of an AI based CAD system in solid lung nodule detection on chest phantom radiographs compared to radiology residents and fellow radiologists.

Authors:  Alan A Peters; Amanda Decasper; Jaro Munz; Jeremias Klaus; Laura I Loebelenz; Maximilian Korbinian Michael Hoffner; Cynthia Hourscht; Johannes T Heverhagen; Andreas Christe; Lukas Ebner
Journal:  J Thorac Dis       Date:  2021-05       Impact factor: 3.005

Review 8.  The Role of Radiomics in Lung Cancer: From Screening to Treatment and Follow-Up.

Authors:  Radouane El Ayachy; Nicolas Giraud; Paul Giraud; Catherine Durdux; Philippe Giraud; Anita Burgun; Jean Emmanuel Bibault
Journal:  Front Oncol       Date:  2021-05-05       Impact factor: 6.244

9.  Assessing the predictive accuracy of lung cancer, metastases, and benign lesions using an artificial intelligence-driven computer aided diagnosis system.

Authors:  Kunwei Li; Kunfeng Liu; Yinghua Zhong; Mingzhu Liang; Peixin Qin; Haijun Li; Rongguo Zhang; Shaolin Li; Xueguo Liu
Journal:  Quant Imaging Med Surg       Date:  2021-08

10.  A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.

Authors:  Ayşegül Gürsoy Çoruh; Bülent Yenigün; Çağlar Uzun; Yusuf Kahya; Emre Utkan Büyükceran; Atilla Elhan; Kaan Orhan; Ayten Kayı Cangır
Journal:  Br J Radiol       Date:  2021-06-11       Impact factor: 3.629

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