Computer-Aided Diagnosis of Lung Nodules on CT Scans

Rationale and Objectives

The aim of this study was to evaluate the effect of computer-aided diagnosis (CAD) on radiologists’ estimates of the likelihood of malignancy of lung nodules on computed tomographic (CT) imaging.

Methods and Materials

A total of 256 lung nodules (124 malignant, 132 benign) were retrospectively collected from the thoracic CT scans of 152 patients. An automated CAD system was developed to characterize and provide malignancy ratings for lung nodules on CT volumetric images. An observer study was conducted using receiver-operating characteristic analysis to evaluate the effect of CAD on radiologists’ characterization of lung nodules. Six fellowship-trained thoracic radiologists served as readers. The readers rated the likelihood of malignancy on a scale of 0% to 100% and recommended appropriate action first without CAD and then with CAD. The observer ratings were analyzed using the Dorfman-Berbaum-Metz multireader, multicase method.

Results

The CAD system achieved a test area under the receiver-operating characteristic curve ( A z ) of 0.857 ± 0.023 using the perimeter, two nodule radii measures, two texture features, and two gradient field features. All six radiologists obtained improved performance with CAD. The average A z of the radiologists improved significantly ( P < .01) from 0.833 (range, 0.817–0.847) to 0.853 (range, 0.834–0.887).

Conclusion

CAD has the potential to increase radiologists’ accuracy in assessing the likelihood of malignancy of lung nodules on CT imaging.

The use of computed tomographic (CT) imaging for lung cancer screening is an area of active research. CT imaging has been shown to be more sensitive for lung nodule detection than chest x-ray, especially for smaller nodules . Henschke et al reported a 92% survival rate among patients who underwent surgical resection for detected stage I lung cancers. Sobue et al reported an almost 100% 5-year survival rate for patients with nodules < 9 mm in size. These data suggest the benefits of earlier intervention with early detection. It is expected that the National Lung Screening Trial , which is a randomized, controlled study of >50,000 enrolled patients, will provide more definitive results as to whether early detection with CT imaging compared to CXR will lead to reduced patient mortality.

The higher sensitivity of CT imaging results in an increase in the number of nodules detected and thus an increase in the nodules that need to be followed up and managed. This may require expensive diagnostic tests such as follow-up CT scans and biopsies. Multiple–detector row CT technology has resulted in thinner slices and higher resolution. However, the large number of images that radiologists must interpret greatly increases their workload. Despite higher quality images, Swensen et al reported that as many as 50% of nodules resected at surgery are benign, signifying the difficulty radiologists have in determining whether a lung nodule is malignant or not on the basis of CT and other clinical information. This emphasizes the importance of developing methods that can assist radiologists in characterizing nodules.

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Methods and materials

Collection of CT Studies

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Nodule Selection

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CAD System

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Observer Study

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Statistical Analysis

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Results

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Table 1

Individual and Average Performance of the Observers in Terms of A z without and with CAD

Observer_A_ z without CAD_A_ z with CAD 1 0.817 ± 0.026 0.846 ± 0.024 2 0.845 ± 0.025 0.857 ± 0.024 3 0.843 ± 0.024 0.847 ± 0.024 4 0.829 ± 0.025 0.853 ± 0.023 5 0.847 ± 0.024 0.877 ± 0.021 6 0.817 ± 0.026 0.834 ± 0.025 Average ∗ 0.833 0.854

A z , area under the receiver-operating characteristic curve; CAD, computer-aided diagnosis.

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Table 2

The Number of Times on Average That Observers Changed Their LM Estimates and Recommended Actions with CAD

LM Change Action Change Total 126 ± 46.8 (49 ± 17%) 10.8 ± 5.8 Correct 95 ± 34.0 (37 ± 13%) 6.8 ± 2.5 (3 ± 1%) Incorrect 31 ± 18.2 (12 ± 7%) 4 ± 3.6 (2 ± 1%)

CAD, computer-aided diagnosis; LM, likelihood of malignancy.

Values in parentheses are percentages relative to the total number of nodules.

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Table 3

Average Performance of the Observers in Terms of A z and A0.9z A

0.9 for the Entire Data Set, the Primary and Metastatic Subsets, and the Data Set Excluding the Eight Masses > 30mm in Diameter

Average A z Average A0.9z A

0.9 Data Set Without CAD With CAD Without CAD With CAD All 256 nodules 0.833 0.853 0.390 0.456 Primary cancers 0.823 0.846 0.338 0.415 Metastatic cancers 0.849 0.861 0.493 0.535 Excluding masses > 30 mm in diameter 0.832 0.850 0.392 0.455

A z , area under the receiver-operating characteristic curve; A0.9z A

0.9 , partial A z (the area under the receiver-operating characteristic curve above a true-positive fraction value of 0.9) CAD, computer-aided diagnosis.

All improvements with CAD were statistically significant ( P < .05), except for the metastatic cancer subset ( P = .06).

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Discussion

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Acknowledgment

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References

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Computer-Aided Diagnosis of Lung Nodules on CT Scans

Rationale and Objectives

Methods and Materials

Results

Conclusion

Methods and materials

Collection of CT Studies

Nodule Selection

CAD System

Observer Study

Statistical Analysis

Results

Discussion

Acknowledgment

References

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