Radiologists' performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy.

OBJECTIVE. The purpose of our study was to evaluate whether a computer-aided diagnosis (CAD) scheme can assist radiologists in distinguishing small benign from malignant lung nodules on high-resolution CT (HRCT). MATERIALS AND METHODS. We developed an automated computerized scheme for Giang Lil determining the likelihood of malignancy of lung nodules on multiple HRCT slices; the likelihood hood estimate was obtained from various objective features of the nodules using linear discriminent analysis. The data set used in this observer study consisted of 28 primary lung cancers (6-20 mm) 28 benign nodules. Cancer cases included nodules with pure ground-glass opacity, Heber MacMahon mixed ground-glass opacity, and solid opacity. Benign nodules were selected by matching their size and pattern to the malignant nodules. Consecutive region-of-interest images for each nodule on HRCT were displayed for interpretation in stacked mode on a cathode ray tube monitor. The images were presented to 16 radiologists-first without and then with the computer output-who were asked to indicate their confidence level regarding the malignancy of a nodule. Performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS. The area under the ROC curve (A(Z) value) of the CAD scheme alone was 0.831 for distinguishing benign from malignant nodules. The average A(z) value for radiologists was improved with the aid of the CAD scheme from 0.785 to 0.853 by a statistically significant level (p = 0.016). The radiologists' diagnostic performance with the CAD scheme was more accurate than that of the CAD scheme alone (p < 0.05) and also that of radiologists alone. CONCLUSION. CAD has the potential to improve radiologists' diagnostic accuracy in distinguishing small benign nodules from malignant ones on HRCT.