title: Small solitary pulmonary nodules (< or =1 cm) detected at population-based CT screening for lung cancer: Reliable high-resolution CT features of benign lesions.
abstract: OBJECTIVE
  We assessed thin-section CT features specific to benignity in solitary pulmonary nodules of 1 cm or smaller that were detected at population-based CT screening for lung cancer.
  
  
  METHODS
  Two reviewers independently made qualitative (presence or absence of lobulation, spiculation, air bronchogram, cavity, satellite lesions, pleural tag, concave margins, polygonal shape, and peripheral subpleural lesion) and quantitative (lesion size, percentage of ground-glass opacity areas, and two- and three-dimensional ratios of lesion) assessments in CT images of 72 nodules (25 lung cancers, seven atypical adenomatous hyperplasias, and 40 benign lesions). Optimal criteria specific to benignity were studied.
  
  
  RESULTS
  The prevalence of polygonal shape (p = 0.005 and p = 0.019, reviewer 1 and reviewer 2), peripheral subpleural lesion (p = 0.011 and p = 0.033), a predominantly solid lesion (p < 0.001 and p < 0.001), and three-dimensional ratios (p < 0.001 and p < 0.001) were greater in benign lesions than in malignancies. The prevalence of a predominantly solid lesion (p < 0.001 and p < 0.001) was greater in benign lesions than in atypical adenomatous hyperplasias, and the prevalence of a peripheral subpleural lesion (p = 0.004 and p = 0.012) was greater in atypical adenomatous hyperplasias than in malignancies. Using a single CT feature, polygonal shape and a three-dimensional ratio of greater than 1.78 showed 100% specificity for both reviewers. Among all combinations of CT findings specific to benignity, a combined criterion of a predominantly solid lesion and peripheral subpleural lesion or polygonal shape or the three-dimensional ratio attained the highest sensitivity (63% and 60%) for both reviewers.
  
  
  CONCLUSIONS
  A combined criterion of CT features was optimal for predicting benign pulmonary lesions.
authors.affiliation: Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto 390-8621, Japan.
authors.forename: Shodayu
authors.initials: S
authors.lastname: Takashima
authors.forename: Shusuke
authors.initials: S
authors.lastname: Sone
authors.forename: Feng
authors.initials: F
authors.lastname: Li
authors.forename: Yuichiro
authors.initials: Y
authors.lastname: Maruyama
authors.forename: Minoru
authors.initials: M
authors.lastname: Hasegawa
authors.forename: Tsuyoshi
authors.initials: T
authors.lastname: Matsushita
authors.forename: Fumiyoshi
authors.initials: F
authors.lastname: Takayama
authors.forename: Masumi
authors.initials: M
authors.lastname: Kadoya
mesh_terms: D000230:Adenocarcinoma / Q000000981:diagnostic imaging / Q000453:epidemiology; D002282:Adenocarcinoma, Bronchiolo-Alveolar / Q000000981:diagnostic imaging / Q000453:epidemiology; D000236:Adenoma / Q000000981:diagnostic imaging / Q000473:pathology; D000328:Adult; D000368:Aged; D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging / Q000453:epidemiology; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D006965:Hyperplasia; D007091:Image Processing, Computer-Assisted; D015994:Incidence; D007564:Japan / Q000453:epidemiology; D008168:Lung / Q000000981:diagnostic imaging / Q000473:pathology; D008171:Lung Diseases / Q000000981:diagnostic imaging / Q000453:epidemiology; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000453:epidemiology; D008297:Male; D008403:Mass Screening / Q000706:statistics & numerical data; D008875:Middle Aged; D015588:Observer Variation; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000453:epidemiology; D036542:Tomography, Spiral Computed