Literature DB >> 29206596

Value of a Computer-aided Detection System Based on Chest Tomosynthesis Imaging for the Detection of Pulmonary Nodules.

Yoshitake Yamada1, Eisuke Shiomi1, Masahiro Hashimoto1, Takayuki Abe1, Masaki Matsusako1, Yukihisa Saida1, Kenji Ogawa1.   

Abstract

Purpose To assess the value of a computer-aided detection (CAD) system for the detection of pulmonary nodules on chest tomosynthesis images. Materials and Methods Fifty patients with and 50 without pulmonary nodules underwent both chest tomosynthesis and multidetector computed tomography (CT) on the same day. Fifteen observers (five interns and residents, five chest radiologists, and five abdominal radiologists) independently evaluated tomosynthesis images of 100 patients for the presence of pulmonary nodules in a blinded and randomized manner, first without CAD, then with the inclusion of CAD marks. Multidetector CT images served as the reference standard. Free-response receiver operating characteristic analysis was used for the statistical analysis. Results The pooled diagnostic performance of 15 observers was significantly better with CAD than without CAD (figure of merit [FOM], 0.74 vs 0.71, respectively; P = .02). The average true-positive fraction and false-positive rate per all cases with CAD were 0.56 and 0.26, respectively, whereas those without CAD were 0.47 and 0.20, respectively. Subanalysis showed that the diagnostic performance of interns and residents was significantly better with CAD than without CAD (FOM, 0.70 vs 0.62, respectively; P = .001), whereas for chest radiologists and abdominal radiologists, the FOM with CAD values were greater but not significantly: 0.80 versus 0.78 (P = .38) and 0.74 versus 0.73 (P = .65), respectively. Conclusion CAD significantly improved diagnostic performance in the detection of pulmonary nodules on chest tomosynthesis images for interns and residents, but provided minimal benefit for chest radiologists and abdominal radiologists. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 29206596     DOI: 10.1148/radiol.2017170405

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


  3 in total

1.  Validation of deep learning-based computer-aided detection software use for interpretation of pulmonary abnormalities on chest radiographs and examination of factors that influence readers' performance and final diagnosis.

Authors:  Naoki Toda; Masahiro Hashimoto; Yu Iwabuchi; Misa Nagasaka; Ryo Takeshita; Minoru Yamada; Yoshitake Yamada; Masahiro Jinzaki
Journal:  Jpn J Radiol       Date:  2022-09-19       Impact factor: 2.701

2.  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

3.  Computer-aided diagnosis system of thyroid nodules ultrasonography: Diagnostic performance difference between computer-aided diagnosis and 111 radiologists.

Authors:  Tingting Li; Zirui Jiang; Man Lu; Shibin Zou; Minggang Wu; Ting Wei; Lu Wang; Juan Li; Ziyue Hu; Xueqing Cheng; Jifen Liao
Journal:  Medicine (Baltimore)       Date:  2020-06-05       Impact factor: 1.817

  3 in total

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