Literature DB >> 25592026

Computer-aided nodule detection system: results in an unselected series of consecutive chest radiographs.

Feng Li1, Roger Engelmann2, Samuel G Armato2, Heber MacMahon2.   

Abstract

RATIONALE AND
OBJECTIVES: To evaluate the performance of a computer-aided detection (CAD) system with bone suppression imaging when applied to unselected consecutive chest radiographs (CXRs) with computed tomography (CT) correlation.
MATERIALS AND METHODS: This study included 586 consecutive patients with standard or portable CXRs who had a chest CT scan on the same day. Among the 586 CXRs, 438 had various abnormalities, including 46 CXRs with 66 lung nodules, and 148 CXRs had no significant abnormalities. A commercially available CAD system was applied to all 586 CXRs. True nodules and false positives (FPs) marked on CXRs by the CAD system were evaluated based on the corresponding chest CT findings.
RESULTS: The CAD system marked 47 of 66 (71%) lung nodules in this consecutive series of CXRs. The mean FP rate per image was 1.3 across all 586 CXRs, with 1.5 FPs per image on the 438 abnormal CXRs and 0.8 FPs per image on the 148 normal CXRs. A total of 41% of the 752 FP marks were related to non-nodule pathologic findings.
CONCLUSIONS: A currently available CAD system marked 71% of radiologist-identified lung nodules in a large consecutive series of CXRs, and 41% of "false" marks were caused by pathologic findings.
Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Lung nodule; chest computed tomography (CT); chest radiography (CXR); computer-aided detection (CAD); lung abnormality

Mesh:

Year:  2015        PMID: 25592026     DOI: 10.1016/j.acra.2014.11.008

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  7 in total

Review 1.  Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

Authors:  Feng Li
Journal:  Radiol Phys Technol       Date:  2015-05-17

2.  Reproducibility of abnormality detection on chest radiographs using convolutional neural network in paired radiographs obtained within a short-term interval.

Authors:  Yongwon Cho; Young-Gon Kim; Sang Min Lee; Joon Beom Seo; Namkug Kim
Journal:  Sci Rep       Date:  2020-10-15       Impact factor: 4.379

Review 3.  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

4.  A novel bone suppression algorithm in intensity-based 2D/3D image registration for real-time tumor motion monitoring: Development and phantom-based validation.

Authors:  Ingo Gulyas; Petra Trnkova; Barbara Knäusl; Joachim Widder; Dietmar Georg; Andreas Renner
Journal:  Med Phys       Date:  2022-06-06       Impact factor: 4.506

5.  Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs.

Authors:  Feng Li; Samuel G Armato; Roger Engelmann; Thomas Rhines; Jennie Crosby; Li Lan; Maryellen L Giger; Heber MacMahon
Journal:  J Digit Imaging       Date:  2021-07-29       Impact factor: 4.903

6.  A robust convolutional neural network for lung nodule detection in the presence of foreign bodies.

Authors:  Manuel Schultheiss; Sebastian A Schober; Marie Lodde; Jannis Bodden; Juliane Aichele; Christina Müller-Leisse; Bernhard Renger; Franz Pfeiffer; Daniela Pfeiffer
Journal:  Sci Rep       Date:  2020-07-31       Impact factor: 4.379

7.  Short-term Reproducibility of Pulmonary Nodule and Mass Detection in Chest Radiographs: Comparison among Radiologists and Four Different Computer-Aided Detections with Convolutional Neural Net.

Authors:  Young-Gon Kim; Yongwon Cho; Chen-Jiang Wu; Sejin Park; Kyu-Hwan Jung; Joon Beom Seo; Hyun Joo Lee; Hye Jeon Hwang; Sang Min Lee; Namkug Kim
Journal:  Sci Rep       Date:  2019-12-10       Impact factor: 4.379

  7 in total

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