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

<bold>Background: </bold>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. <bold>Purpose: </bold>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. <bold>Material and Methods: </bold>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. <bold>Results: </bold>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. <bold>Conclusion: </bold>Visual assessment and SUVmax on PET-CT correlated well with the histology grading of small peripheral adenocarcinoma of the lung. [ABSTRACT FROM AUTHOR]