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

• Lee KH
• Goo JM
• Park CM
• Lee HJ
• Jin KN

• KMID: 1392934
• DOI: http://doi.org/10.3348/kjr.2012.13.5.564

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

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

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  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.

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  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.

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