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BackgroundMany studies have reported that transverse computed tomography (CT) imaging findings correlate with prognosis of patients with small peripheral lung neoplasm with lepidic growth. However, no studies have examined this correlation with the aid of three-dimensional (3D) CT data.PurposeTo determine the most efficacious imaging factor for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis.Material and MethodsWe evaluated CT findings, nodule patterns, SUVmaxon FDG-PET/CT, as well as nodule volume and ratios of solid parts to nodule volume that were semi-automatically measured on CT images of 64 pulmonary nodules of ≤2 cm in 60 consecutive patients (24 men and 36 women; mean age, 65 years). For logistic modeling, we used all of the significant factors observed between the neoplasms with good and with poor prognosis as independent variables to estimate the statistically significant factors for discriminating invasive adenocarcinomas with lepidic growth (lesions with poor prognosis, n = 42) from the other neoplasms, including preinvasive lesions (lesions with good prognosis, n = 22), resulting in a recommendation for the optimal criterion for predicting lesions with poor prognosis.ResultsThe logistic regression model identified the ratio of the solid part to the whole volume of a pulmonary nodule as the only significant factor (P = 0.04) for differentiating lepidic growth type lung neoplasms with good prognosis from those with poor prognosis. A ratio of 0.238 or more showed the highest discriminatory accuracy of 84% with 91% sensitivity and 76% specificity.ConclusionComputer-aided analyses of pulmonary nodules proved most useful for establishing the optimal criterion for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis.

BackgroundGround-glass opacity (GGO) is reported to be characteristic to lepidic growth of neoplasm in subsolid nodules. In solid nodules of lung cancer, however, there is no characteristic feature to be reported.PurposeTo study if there are any thin-section CT findings characteristic to tumor histology or if they are only related to tumor size in solid nodules of the lung cancer.Material and MethodsThis study included 106 solid peripheral lung cancers of 3 cm or smaller (56 adenocarcinomas, 33 squamous cell carcinomas, and 17 small cell carcinomas) in which 16-slice CT with 1 mm collimation was performed before surgery. Six morphologic findings (presence or absence of lobulation, coarse spiculation, air bronchogram, cavity, pleural tag, and pleural-based lesion) and four measurements (ratio of the greatest transverse and vertical diameter to the shortest transverse diameter and density of lobulation and coarse spiculation) on thin-section CT images were evaluated. Density of lobulation (coarse spiculation) was defined as the ratio of lobulation (coarse spiculation) number to the greatest transverse diameter of a nodule.ResultsAir bronchogram (P < 0.01) was the only significant factor for predicting lung adenocarcinoma. The prevalence of air bronchogram was significantly greater in adenocarcinoma than in squamous cell carcinoma (P < 0.01) or small cell carcinoma (P < 0.01). As the tumor size advanced, significantly positive linear trends were seen in the prevalence of lobulation (P < 0.01), coarse spiculation (P < 0.01), and pleural tag (P < 0.01), and the mean values of density of lobulation (P < 0.01) and coarse spiculation (P < 0.01), while the significant negative linear trend was seen in the ratio of vertical diameter to the shortest transverse (P = 0.02).ConclusionAir bronchogram on thin-section CT is characteristic feature of solid adenocarcinoma of the lung. However, other thin-section CT findings are irrelevant to tumor histology and related only to tumor size.