Thin-section CT findings in peripheral lung cancer of 3 cm or smaller: are there any characteristic features for predicting tumor histology or do they depend only on tumor size?

BACKGROUND: Ground-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. PURPOSE: To 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 METHODS: This 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. RESULTS: Air 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). CONCLUSION: Air 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.

Differentiation of lung neoplasms with lepidic growth and good prognosis from those with poor prognosis using computer-aided 3D volumetric CT analysis and FDG-PET.

BACKGROUND: Many 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. PURPOSE: To determine the most efficacious imaging factor for differentiation of lepidic growth type lung neoplasms with good prognosis from those with poor prognosis. MATERIAL AND METHODS: We evaluated CT findings, nodule patterns, SUVmax on 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. RESULTS: The 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. CONCLUSION: Computer-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.

F18-FDG PET-CT analyses of small peripheral adenocarcinoma of the lung.

BACKGROUND: Radiological discrimination of histologic subtypes of small peripheral adenocarcinoma of the lung is clinically important. Although there are many articles in which CT findings were used for this purpose, there are only a few reports on the capability of FDG PET-CT findings for histologic classification of this tumor. PURPOSE: To investigate the correlation between visual assessment or maximum standard uptake values (SUVmax) on F18-FDG PET-CT and histology grading of small peripheral adenocarcinoma of the lung. MATERIAL AND METHODS: Proportions of positive PET-CT diagnoses and SUVmax were retrospectively reviewed on 96 solitary pulmonary nodules of ≤2 cm in 90 consecutive patients. Tumors were classified into four groups according to Noguchi's classification (group 1 [n = 10], atypical adenomatous hyperplasia and type A tumors; group 2 [n = 12], type B tumors; group 3 [n = 42], type C tumors; group 4 [n = 32], types D, E, and F tumors). Proportions of positive PET-CT diagnoses and mean SUVmax of lesions among four groups were compared using trend tests to examine if there is a significant linear correlation with the progression of the histology grading of tumors. Then, an optimal threshold of SUVmax was proposed to best discriminate tumors of poor (groups 3 and 4) from good (groups 1 and 2) prognosis. RESULTS: There was a significant linear trend for both visual assessment (P < 0.01) and SUVmax (P < 0.01). A SUVmax of 0.42 showed the highest accuracy of 84% with 95% sensitivity and 50% specificity for predicting tumors of poor prognosis. A SUVmax of 2.05 showed 100% specificity with 49% sensitivity, and 60% accuracy. Positive visual diagnoses showed accuracy of 83% with 90% sensitivity and 59% specificity. CONCLUSION: Visual assessment and SUVmax on PET-CT correlated well with the histology grading of small peripheral adenocarcinoma of the lung.

Membranous nephropathy after allogeneic stem cell transplantation: report of 2 cases.

Nephrotic syndrome (NS) is a rare complication of allogeneic hematopoietic stem cell transplantation (allo-HSCT). The cases of 2 patients who developed NS after receiving allo-HSCT for chronic myelogenous leukemia and acute lymphoblastic leukemia are described. In both cases, renal biopsy revealed membranous nephropathy (MN), and the patients achieved remission after treatment with prednisolone. Previously reported cases and our experience suggest that most NS patients show MN in histologic tests after allo-HSCT and that its development is related to graft-versus-host disease. Early treatment with steroids seems effective for resolving symptoms of NS and improving renal function.

Diagnosis of regional node metastases in lung cancer with computer-aided 3D measurement of the volume and CT-attenuation values of lymph nodes.

RATIONALE AND OBJECTIVES: The aim of this study is to assess the usefulness of computer-aided three-dimensional (3D) measurement of volume and computed tomography (CT) attenuation values of nodes for diagnosing nodal metastases of lung cancer. MATERIALS AND METHODS: We measured three diameters, their ratios, volume, and CT values in 3D images of 191 nodes (64 malignant; 162 of <1 cm in short diameter) in 26 consecutive patients who underwent contrast-enhanced, thin-section, multidetector row CT before surgery. We separately studied statistically significant factors in a group of all nodes and in another group of nodes of <1 cm in short diameter with logistic modeling and evaluated their diagnostic accuracy. RESULTS: Significant factors were CT values (P < .001) and short diameter (P = .001) for the total node group, and CT values (P = .030) and 3D volume (P = .035) for the <1 cm node group. Optimal 83% accuracy was obtained with a criterion of short diameter of >7.4 mm and CT values of >103 Hounsfield unit (HU) for the total node group, whereas optimal 76% accuracy was obtained with a criterion of 3D volume of >1282 mm(3) or CT values of >103 HU for the <1 cm node group. CONCLUSION: 3D measurement may be useful for diagnosing nodal metastases.