Korean J Radiol.  2012 Oct;13(5):564-571. 10.3348/kjr.2012.13.5.564.

Computer-Aided Detection of Malignant Lung Nodules on Chest Radiographs: Effect on Observers' Performance

Affiliations
  • 1Department of Radiology, Seoul National University College of Medicine, Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul 110-744, Korea. jmgoo@plaza.snu.ac.kr
  • 2Cancer Research Institute, Seoul National University, Seoul 110-799, Korea.

Abstract


OBJECTIVE
To evaluate the effect of computer-aided detection (CAD) system on observer performance in the detection of malignant lung nodules on chest radiograph.
MATERIALS AND METHODS
Two hundred chest radiographs (100 normal and 100 abnormal with malignant solitary lung nodules) were evaluated. With CT and histological confirmation serving as a reference, the mean nodule size was 15.4 mm (range, 7-20 mm). Five chest radiologists and five radiology residents independently interpreted both the original radiographs and CAD output images using the sequential testing method. The performances of the observers for the detection of malignant nodules with and without CAD were compared using the jackknife free-response receiver operating characteristic analysis.
RESULTS
Fifty-nine nodules were detected by the CAD system with a false positive rate of 1.9 nodules per case. The detection of malignant lung nodules significantly increased from 0.90 to 0.92 for a group of observers, excluding one first-year resident (p = 0.04). When lowering the confidence score was not allowed, the average figure of merit also increased from 0.90 to 0.91 (p = 0.04) for all observers after a CAD review. On average, the sensitivities with and without CAD were 87% and 84%, respectively; the false positive rates per case with and without CAD were 0.19 and 0.17, respectively. The number of additional malignancies detected following true positive CAD marks ranged from zero to seven for the various observers.
CONCLUSION
The CAD system may help improve observer performance in detecting malignant lung nodules on chest radiographs and contribute to a decrease in missed lung cancer.

Keyword

Computer-aided detection; Lung nodules; Lung cancer; Chest radiograph

MeSH Terms

Aged
Algorithms
Diagnosis, Computer-Assisted/*instrumentation
Diagnosis, Differential
Female
Humans
Image Interpretation, Computer-Assisted
Lung Neoplasms/*radiography
Male
Middle Aged
Observer Variation
ROC Curve
*Radiography, Thoracic
Reproducibility of Results
Tomography, X-Ray Computed

Figure

  • Fig. 1 Chest radiograph shows scatterplot of locations of 100 lung cancers.

  • Fig. 2 72-year-old woman with 16 mm adenocarcinoma in left lower lobe. There are four computer-aided detection (CAD) marks among which only mark in left lower lung is true positive. Small calcified nodule in right upper lobe was excluded from analysis. At initial reading, three radiologists and two residents detected true lesion. After review of CAD marks, one radiologist and two residents accepted true CAD mark, while one radiologist and one resident rejected true CAD mark. One resident detected one false positive lesion and added one more false positive lesion after review of CAD marks.

  • Fig. 3 61-year-old man with 11 mm adenocarcinoma in right upper lobe. There is one computer-aided detection (CAD) mark in right upper lobe which is true positive. At initial reading, two radiologists and one resident detected true lesion. After review of CAD marks, two radiologists and two residents accepted true CAD mark, while one radiologist and two residents rejected true CAD mark. Two residents detected one false positive lesion each, but rejected false positive lesion after review of CAD marks.


Reference

1. Muhm JR, Miller WE, Fontana RS, Sanderson DR, Uhlenhopp MA. Lung cancer detected during a screening program using four-month chest radiographs. Radiology. 1983. 148:609–615.
2. Austin JH, Romney BM, Goldsmith LS. Missed bronchogenic carcinoma: radiographic findings in 27 patients with a potentially resectable lesion evident in retrospect. Radiology. 1992. 182:115–122.
3. Monnier-Cholley L, Arrivé L, Porcel A, Shehata K, Dahan H, Urban T, et al. Characteristics of missed lung cancer on chest radiographs: a French experience. Eur Radiol. 2001. 11:597–605.
4. Quekel LG, Kessels AG, Goei R, van Engelshoven JM. Miss rate of lung cancer on the chest radiograph in clinical practice. Chest. 1999. 115:720–724.
5. Choi EJ, Jin GY, Han YM, Lee YS, Kweon KS. Solitary pulmonary nodule on helical dynamic CT scans: analysis of the enhancement patterns using a computer-aided diagnosis (CAD) system. Korean J Radiol. 2008. 9:401–408.
6. Song KD, Chung MJ, Kim HC, Jeong SY, Lee KS. Usefulness of the CAD system for detecting pulmonary nodule in real clinical practice. Korean J Radiol. 2011. 12:163–168.
7. Goo JM. A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. Korean J Radiol. 2011. 12:145–155.
8. Kakeda S, Moriya J, Sato H, Aoki T, Watanabe H, Nakata H, et al. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am J Roentgenol. 2004. 182:505–510.
9. Shiraishi J, Li F, Doi K. Computer-aided diagnosis for improved detection of lung nodules by use of posterior-anterior and lateral chest radiographs. Acad Radiol. 2007. 14:28–37.
10. Bley TA, Baumann T, Saueressig U, Pache G, Treier M, Schaefer O, et al. Comparison of radiologist and CAD performance in the detection of CT-confirmed subtle pulmonary nodules on digital chest radiographs. Invest Radiol. 2008. 43:343–348.
11. He Q, He W, Wang K, Ma D. Effect of multiscale processing in digital chest radiography on automated detection of lung nodule with a computer assistance system. J Digit Imaging. 2008. 21:Suppl 1. S164–S170.
12. Kasai S, Li F, Shiraishi J, Doi K. Usefulness of computer-aided diagnosis schemes for vertebral fractures and lung nodules on chest radiographs. AJR Am J Roentgenol. 2008. 191:260–265.
13. Li F, Engelmann R, Metz CE, Doi K, MacMahon H. Lung cancers missed on chest radiographs: results obtained with a commercial computer-aided detection program. Radiology. 2008. 246:273–280.
14. van Beek EJ, Mullan B, Thompson B. Evaluation of a real-time interactive pulmonary nodule analysis system on chest digital radiographic images: a prospective study. Acad Radiol. 2008. 15:571–575.
15. White CS, Flukinger T, Jeudy J, Chen JJ. Use of a computer-aided detection system to detect missed lung cancer at chest radiography. Radiology. 2009. 252:273–281.
16. de Hoop B, De Boo DW, Gietema HA, van Hoorn F, Mearadji B, Schijf L, et al. Computer-aided detection of lung cancer on chest radiographs: effect on observer performance. Radiology. 2010. 257:532–540.
17. Chakraborty DP. Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. Acad Radiol. 2006. 13:1187–1193.
18. Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys. 2008. 35:5799–5820.
19. Li Q, Li F, Shiraishi J, Katsuragawa S, Sone S, Doi K. Investigation of new psychophysical measures for evaluation of similar images on thoracic computed tomography for distinction between benign and malignant nodules. Med Phys. 2003. 30:2584–2593.
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