title: Pulmonary Nodules: growth rate assessment in patients by using serial CT and three-dimensional volumetry.
abstract: OBJECTIVE
  To determine the precision of a three-dimensional (3D) method for measuring the growth rate of solid and subsolid nodules and its ability to detect abnormal growth rates.
  
  
  METHODS
  This study was approved by the Institutional Research Board and was HIPAA compliant. Informed consent was waived. The growth rates of 123 lung nodules in 59 patients who had undergone lung cancer screening computed tomography (CT) were measured by using a 3D semiautomated computer-assisted volume method. Clinical stability was established with long-term CT follow-up (mean, 6.4 years±1.9 [standard deviation]; range, 2.0-8.5 years). A mean of 4.1 CT examinations per patient±1.2 (range, two to seven CT examinations per patient) was analyzed during 2.4 years±0.5 after baseline CT. Nodule morphology, attenuation, and location were characterized. The analysis of standard deviation of growth rate in relation to time between scans yielded a normative model for detecting abnormal growth.
  
  
  RESULTS
  Growth rate precision increased with greater time between scans. Overall estimate for standard deviation of growth rate, on the basis of 939 growth rate determinations in clinically stable nodules, was 36.5% per year. Peripheral location (P=.01; 37.1% per year vs 25.6% per year) and adjacency to pleural surface (P=.05; 38.9% per year vs 34.0% per year) significantly increased standard deviation of growth rate. All eight malignant nodules had an abnormally high growth rate detected. By using 3D volumetry, growth rate-based diagnosis of malignancy was made at a mean of 183 days±158, compared with radiologic or clinical diagnosis at 344 days±284.
  
  
  CONCLUSIONS
  A normative model derived from the variability of growth rates of nodules that were stable for an average of 6.4 years may enable identification of lung cancer.
authors.affiliation: Department of Radiology, New York University Langone Medical Center, 560 1st Ave, New York, NY 10016, USA. jane.ko@nyumc.org
authors.forename: Jane P
authors.initials: JP
authors.lastname: Ko
authors.forename: Erika J
authors.initials: EJ
authors.lastname: Berman
authors.forename: Manmeen
authors.initials: M
authors.lastname: Kaur
authors.forename: James S
authors.initials: JS
authors.lastname: Babb
authors.forename: Elan
authors.initials: E
authors.lastname: Bomsztyk
authors.forename: Alissa K
authors.initials: AK
authors.lastname: Greenberg
authors.forename: David P
authors.initials: DP
authors.lastname: Naidich
authors.forename: Henry
authors.initials: H
authors.lastname: Rusinek
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008175:Lung Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed / Q000379:methods*