title: Development and validation of a prediction model for measurement variability of lung nodule volumetry in patients with pulmonary metastases.
abstract: OBJECTIVE
  To develop a prediction model for the variability range of lung nodule volumetry and validate the model in detecting nodule growth.
  
  
  METHODS
  For model development, 50 patients with metastatic nodules were prospectively included. Two consecutive CT scans were performed to assess volumetry for 1,586 nodules. Nodule volume, surface voxel proportion (SVP), attachment proportion (AP) and absolute percentage error (APE) were calculated for each nodule and quantile regression analyses were performed to model the 95% percentile of APE. For validation, 41 patients who underwent metastasectomy were included. After volumetry of resected nodules, sensitivity and specificity for diagnosis of metastatic nodules were compared between two different thresholds of nodule growth determination: uniform 25% volume change threshold and individualized threshold calculated from the model (estimated 95% percentile APE).
  
  
  RESULTS
  SVP and AP were included in the final model: Estimated 95% percentile APE = 37.82 · SVP + 48.60 · AP-10.87. In the validation session, the individualized threshold showed significantly higher sensitivity for diagnosis of metastatic nodules than the uniform 25% threshold (75.0% vs. 66.0%, P = 0.004) CONCLUSION: Estimated 95% percentile APE as an individualized threshold of nodule growth showed greater sensitivity in diagnosing metastatic nodules than a global 25% threshold.
  
  
  CONCLUSIONS
  • The 95 % percentile APE of a particular nodule can be predicted. • Estimated 95 % percentile APE can be utilized as an individualized threshold. • More sensitive diagnosis of metastasis can be made with an individualized threshold. • Tailored nodule management can be provided during nodule growth follow-up.
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.|Deparment of Radiology, Armed Forces Seoul Hospital, Seoul, Korea.
authors.forename: Eui Jin
authors.initials: EJ
authors.lastname: Hwang
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea. jmgoo@plaza.snu.ac.kr.|Cancer Research Institute, Seoul National University, Seoul, Korea. jmgoo@plaza.snu.ac.kr.
authors.forename: Jin Mo
authors.identifier: http://orcid.org/0000-0003-1791-7942
authors.initials: JM
authors.lastname: Goo
authors.affiliation: School of Computer Science and Engineering, Seoul National University, Seoul, Korea.
authors.forename: Jihye
authors.initials: J
authors.lastname: Kim
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.|Cancer Research Institute, Seoul National University, Seoul, Korea.
authors.forename: Sang Joon
authors.initials: SJ
authors.lastname: Park
authors.affiliation: Medical Research Collaborating Center, Seoul National University Bundang Hospital, Seongnam-si, Gyeonggi-do, Korea.
authors.forename: Soyeon
authors.initials: S
authors.lastname: Ahn
authors.affiliation: Department of Radiology, Seoul National University College of Medicine, and Institute of Radiation Medicine, Seoul National University Medical Research, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Korea.|Cancer Research Institute, Seoul National University, Seoul, Korea.
authors.forename: Chang Min
authors.initials: CM
authors.lastname: Park
authors.affiliation: School of Computer Science and Engineering, Seoul National University, Seoul, Korea.
authors.forename: Yeong-Gil
authors.initials: YG
authors.lastname: Shin
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology* / Q000556:secondary*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011446:Prospective Studies; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging / Q000473:pathology*; D014057:Tomography, X-Ray Computed
keywords: Growth; Lung nodule; Measurement error; Modeling; Volumetry