Discrimination of subtypes of small adenocarcinoma in the lung with thin-section CT

Abstract

We studied the usefulness of thin-section CT in discriminating two categories of adenocarcinoma in the lung. Thin-section CT findings, such as, lesion size, ground-glass opacity (GGO) areas of lesion and presence or absence of lobulation, coarse spiculation, air bronchogram, small air space, or pleural tag of lesion in 62 consecutive patients with 62 adenocarcinomas (35 type A or B tumors (Noguchi's classification) and 27 type C tumors) of ≤20 mm, including 36 women and 26 men with a mean age of 64 years were analyzed. We performed stepwise logistic modeling using all the CT findings as independent variables to estimate the significant factors for discriminating type C from type A or B tumor. Lesion size in type C tumors was significantly ( P<0.001) greater than that in type A or B tumors. GGO areas in type C tumors were significantly ( P<0.001) smaller than that in type A or B tumors. The prevalence of coarse spiculation, air bronchogram, and pleural tag in type C tumors was significantly greater ( P=0.001, 0.010, and <0.001, respectively) than that in type A or B tumors. Logistic modeling revealed that the GGO area was the only significant factor for discriminating two categories ( P<0.001). Using the percentage of GGO areas for predicting type C tumor, 40% or less showed the highest accuracy of 85% with 70% sensitivity and 97% specificity. GGO areas of 30% or less had no false-positive diagnosis (100% specificity) with 81% accuracy but its sensitivity was low (56%). Thin-section CT was useful in discriminating two categories of adenocarcinoma in the lung.