Sensitivity and Specificity of a CAD Solution for Lung Nodule Detection on Chest Radiograph with CTA Correlation

被引:0
|
作者
William Moore
Jennifer Ripton-Snyder
George Wu
Craig Hendler
机构
[1] Stony Brook University Hospital,
来源
Journal of Digital Imaging | 2011年 / 24卷
关键词
Chest CT; chest radiographs; computer assisted detection; lung neoplasms;
D O I
暂无
中图分类号
学科分类号
摘要
The objective of this research was to determine the sensitivity and specificity of a commercially available computer-aided detection (CAD) system for detection of lung nodule on posterior–anterior (PA) chest radiograph in a varied patient population who are referred to computed tomographic angiogram (CTA) of the chest as a reference standard. Patients who had a PA chest radiograph with concomitant CTA of the chest were included in this retrospective study. The PA chest radiograph was analyzed by a CAD device, and results were recorded. A qualitative assessment of the CAD results was performed using a 5-point Likert scale. The CTA was then reviewed to determine if there were correlative nodules. The presence of a correlative nodule between 0.5 cm and 1.5 cm was considered a positive result. The baseline sensitivity of the system was determined to be 0.707 (95% CI = 0.52–0.86), with a specificity of 0.50 (95% CI = 0.38–0.76). Positive predictive value was 0.30 (95% CI = 0.24–0.49), with a negative predictive value of 0.858 (95% CI = 0.82–0.95), and accuracy of 0.555 (95% CI = 0.40–0.66). When excluding nodules that were qualitatively determined by a thoracic radiologist to be false positives, the specificity was 0.781 (95% CI = 0.764–0.839), the positive predictive value was 0.564 (95% CI = 0.491–0.654), the negative predictive value was 0.829 (95% CI = 0.819–0.878), and the accuracy was 0.737 (95% CI = 0.721–0.801). The use of CAD for lung nodule detection on chest radiograph, when used in conjunction with an experienced radiologist, has a very good sensitivity, specificity, and accuracy.
引用
收藏
页码:405 / 410
页数:5
相关论文
共 50 条
  • [31] Computer-aided diagnosis (CAD) for nodule detection in diffuse lung disease
    Digamurthy, SR
    Sharma, A
    Wottram, C
    McGinnis, PJ
    Shepard, JO
    AMERICAN JOURNAL OF ROENTGENOLOGY, 2005, 184 (04) : 1 - 1
  • [32] Computerized Scheme for Lung Nodule Detection in Multi-Projection Chest Radiography
    Guo, Wei
    Li, Qiang
    Boyce, Sarah J.
    McAdams, H. Page
    Shiraishi, Junji
    Doi, Kunio
    Samei, Ehsan
    MEDICAL IMAGING 2012: COMPUTER-AIDED DIAGNOSIS, 2012, 8315
  • [33] A Hybrid Lung Nodule Detection Scheme on Chest X-ray Images
    Orban, G.
    Horvath, G.
    5TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERING, PTS 1 AND 2, 2012, 37 : 603 - 606
  • [34] High sensitivity and specificity of 4D-CTA in the detection of cranial arteriovenous shunts
    Matthijs in ’t Veld
    Rolf Fronczek
    Marlise P. dos Santos
    Marianne A. A. van Walderveen
    Frederick J. A. Meijer
    Peter W. A. Willems
    European Radiology, 2019, 29 : 5961 - 5970
  • [35] Lung vessel suppression and its effect on nodule detection in chest CT scans
    Gu, Xiaomeng
    Xie, Weiyang
    Fang, Qiming
    Zha, Jun
    Li, Qiang
    MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS, 2020, 11314
  • [36] Ultra-Low Dose Chest CT with Denoising for Lung Nodule Detection
    Kerpel, Ariel
    Marom, Edith Michelle
    Green, Michael
    Eifer, Michal
    Konen, Eli
    Mayer, Arnaldo
    Cuellar, Sonia L. Betancourt
    ISRAEL MEDICAL ASSOCIATION JOURNAL, 2021, 23 (09): : 550 - 555
  • [37] Deep Neural Networks Ensemble for Lung Nodule Detection on Chest CT Scans
    Ardimento, Pasquale
    Aversano, Lerina
    Bernardi, Mario Luca
    Cimitile, Marta
    2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2021,
  • [38] Lung Nodule Detection Using Combined Traditional and Deep Models and Chest CT
    Zhang, Junjie
    Huang, Zhaowei
    Huang, Tairan
    Xia, Yong
    Zhang, Yanning
    INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING, 2018, 11266 : 655 - 662
  • [39] Elimination of clavicle shadows to help automatic lung nodule detection on chest radiographs
    Simko, G.
    Orban, G.
    Maday, P.
    Horvath, G.
    4TH EUROPEAN CONFERENCE OF THE INTERNATIONAL FEDERATION FOR MEDICAL AND BIOLOGICAL ENGINEERING, 2009, 22 (1-3): : 488 - 491
  • [40] HIERARCHICAL REPRESENTATIONS FOR LUNG NODULE DETECTION IN CHEST COMPUTER TOMOGRAPHY WITH DEEP NETWORKS
    Zheng, H.
    Chun, J.
    Li, F.
    Ying, H.
    Zhang, M.
    Kenji, S.
    JOURNAL OF INVESTIGATIVE MEDICINE, 2014, 62 (01) : 275 - 275