Literature DB >> 20160602

Dual energy subtraction digital radiography improves performance of a next generation computer-aided detection program.

Jason D Balkman1, Sonali Mehandru, Elena DuPont, Ronald D Novak, Robert C Gilkeson.   

Abstract

PURPOSE: Computer-aided detection (CAD) has shown potential to assist physicians in the detection of lung nodules on chest radiographs, but widespread acceptance has been stymied by high false-positive rates. Few studies have examined the potential for dual energy subtraction (DES) to improve CAD performance.
MATERIALS AND METHODS: Institutional review board approval was obtained, the requirement for informed consent was waived because the study was retrospective, and practices conformed to Health Insurance Portability and Accountability Act regulations. The CAD program was applied retrospectively to dual energy posteroanterior (PA) chest radiographs of 36 patients (17 women, 19 men, mean age 69 y) with 48 pathology proven lung nodules. Results were analyzed to determine the stand-alone CAD program false-positive rates, and sensitivity by nodule subtlety and location. Statistical analysis was performed using the chi(2) or Fisher exact tests for independence of sensitivities between standard PA and DES radiography. Differences in the mean false-positives per image (FPPI) between radiographic modalities were determined using the paired Students t test, and bootstrap confidence intervals were obtained to confirm results.
RESULTS: The sensitivity of the CAD program with the standard PA was 46% (22 of 48 nodules) compared with 67% (32 of 48 nodules) using the DES soft tissue or bone-subtracted view (P=0.064). The average number of FPPI identified by CAD was significantly lower using DES (FPPI(soft tissue) = 1.64) when compared with the standard PA chest radiograph (FPPI(PA) = 2.39) (P<0.01).
CONCLUSIONS: DES has the potential to improve stand-alone CAD performance by both increasing sensitivity for certain subtle lung cancer lesions and decreasing overall CAD false-positive rates.

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Year:  2010        PMID: 20160602     DOI: 10.1097/RTI.0b013e3181aa34ed

Source DB:  PubMed          Journal:  J Thorac Imaging        ISSN: 0883-5993            Impact factor:   3.000


  3 in total

1.  A simple method for identifying image orientation of chest radiographs by use of the center of gravity of the image.

Authors:  Hideo Nose; Yasushi Unno; Masayuki Koike; Junji Shiraishi
Journal:  Radiol Phys Technol       Date:  2012-04-27

2.  [Detection of lung nodules. New opportunities in chest radiography].

Authors:  S Pötter-Lang; S Schalekamp; C Schaefer-Prokop; M Uffmann
Journal:  Radiologe       Date:  2014-05       Impact factor: 0.635

3.  A comparison of computer-aided detection (CAD) effectiveness in pulmonary nodule identification using different methods of bone suppression in chest radiographs.

Authors:  Ronald D Novak; Nicholas J Novak; Robert Gilkeson; Bahar Mansoori; Gunhild E Aandal
Journal:  J Digit Imaging       Date:  2013-08       Impact factor: 4.056

  3 in total

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