Literature DB >> 23617482

Comparison of dual-energy subtraction and electronic bone suppression combined with computer-aided detection on chest radiographs: effect on human observers' performance in nodule detection.

Zsolt Szucs-Farkas1, Alexander Schick, Jennifer L Cullmann, Lukas Ebner, Boglarka Megyeri, Peter Vock, Andreas Christe.   

Abstract

OBJECTIVE: The objective of our study was to compare the effect of dual-energy subtraction and bone suppression software alone and in combination with computer-aided detection (CAD) on the performance of human observers in lung nodule detection.
MATERIALS AND METHODS: One hundred one patients with from one to five lung nodules measuring 5-29 mm and 42 subjects with no nodules were retrospectively selected and randomized. Three independent radiologists marked suspicious-appearing lesions on the original chest radiographs, dual-energy subtraction images, and bone-suppressed images before and after postprocessing with CAD. Marks of the observers and CAD marks were compared with CT as the reference standard. Data were analyzed using nonparametric tests and the jackknife alternative free-response receiver operating characteristic (JAFROC) method.
RESULTS: Using dual-energy subtraction alone (p = 0.0198) or CAD alone (p = 0.0095) improved the detection rate compared with using the original conventional chest radiograph. The combination of bone suppression and CAD provided the highest sensitivity (51.6%) and the original nonenhanced conventional chest radiograph alone provided the lowest (46.9%; p = 0.0049). Dual-energy subtraction and bone suppression provided the same false-positive (p = 0.2702) and true-positive (p = 0.8451) rates. Up to 22.9% of lesions were found only by the CAD program and were missed by the readers. JAFROC showed no difference in the performance between modalities (p = 0.2742-0.5442).
CONCLUSION: Dual-energy subtraction and the electronic bone suppression program used in this study provided similar detection rates for pulmonary nodules. Additionally, CAD alone or combined with bone suppression can significantly improve the sensitivity of human observers for pulmonary nodule detection.

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Year:  2013        PMID: 23617482     DOI: 10.2214/AJR.12.8877

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


  7 in total

1.  A novel bone suppression method that improves lung nodule detection : Suppressing dedicated bone shadows in radiographs while preserving the remaining signal.

Authors:  Jens von Berg; Stewart Young; Heike Carolus; Robin Wolz; Axel Saalbach; Alberto Hidalgo; Ana Giménez; Tomás Franquet
Journal:  Int J Comput Assist Radiol Surg       Date:  2015-09-04       Impact factor: 2.924

2.  Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging.

Authors:  Jiefang Wu; Weiguo Chen; Fengxia Zeng; Le Ma; Weimin Xu; Wei Yang; Genggeng Qin
Journal:  Quant Imaging Med Surg       Date:  2021-10

3.  Feasibility study of dual energy radiographic imaging for target localization in radiotherapy for lung tumors.

Authors:  Jie Huo; Xianfeng Zhu; Yang Dong; Zhiyong Yuan; Ping Wang; Xuemin Wang; Gang Wang; Xin-Hua Hu; Yuanming Feng
Journal:  PLoS One       Date:  2014-09-30       Impact factor: 3.240

4.  Memory bias in observer-performance literature.

Authors:  Tamara Miner Haygood; Samantha Smith; Jia Sun
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-24

Review 5.  Putting artificial intelligence (AI) on the spot: machine learning evaluation of pulmonary nodules.

Authors:  Yasmeen K Tandon; Brian J Bartholmai; Chi Wan Koo
Journal:  J Thorac Dis       Date:  2020-11       Impact factor: 2.895

6.  Bone Suppression on Chest Radiographs for Pulmonary Nodule Detection: Comparison between a Generative Adversarial Network and Dual-Energy Subtraction.

Authors:  Kyungsoo Bae; Dong Yul Oh; Il Dong Yun; Kyung Nyeo Jeon
Journal:  Korean J Radiol       Date:  2022-01       Impact factor: 3.500

7.  Lung cancer screening by single-shot dual-energy subtraction using flat-panel detector.

Authors:  Hiroshi Mogami; Yumiko Onoike; Hiroshi Miyano; Kenji Arakawa; Hiromi Inoue; Kouji Sakae; Toshiaki Kawakami
Journal:  Jpn J Radiol       Date:  2021-06-26       Impact factor: 2.374

  7 in total

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