Review Article| Volume 1, P109-118, September 2019

Quantitative Computed Tomographic Evaluation of Lung Nodules

      Quantitative computed tomographic (CT) evaluation of lung nodules has achieved significant progress through the continued evolution of technical imaging and data-processing capabilities, with an ever-growing body of literature using first- and second-order statistical evaluation.


      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'


      Subscribe to Advances in Clinical Radiology
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect


        • Erasmus J.J.
        • Gladish G.W.
        • Broemeling L.
        • et al.
        Interobserver and intraobserver variability in measurement of non-small-cell carcinoma lung lesions: implications for assessment of tumor response.
        J Clin Oncol. 2003; 21: 2574-2582
        • Revel M.P.
        • Bissery A.
        • Bienvenu M.
        • et al.
        Are two-dimensional CT measurements of small noncalcified pulmonary nodules reliable?.
        Radiology. 2004; 231: 453-458
        • Devaraj A.
        • van Ginneken B.
        • Nair A.
        • et al.
        Use of volumetry for lung nodule management: theory and practice.
        Radiology. 2017; 284: 630-644
        • MacMahon H.
        • Naidich D.P.
        • Goo J.M.
        • et al.
        Guidelines for management of incidental pulmonary nodules detected on CT images: from the Fleischner Society 2017.
        Radiology. 2017; 284: 228-243
        • Horeweg N.
        • van Rosmalen J.
        • Heuvelmans M.A.
        • et al.
        Lung cancer probability in patients with CT-detected pulmonary nodules: a prespecified analysis of data from the NELSON trial of low-dose CT screening.
        Lancet Oncol. 2014; 15: 1332-1341
        • van Klaveren R.J.
        • Oudkerk M.
        • Prokop M.
        • et al.
        Management of lung nodules detected by volume CT scanning.
        N Engl J Med. 2009; 361: 2221-2229
        • Gould M.K.
        • Donington J.
        • Lynch W.R.
        • et al.
        Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians evidence-based clinical practice guidelines.
        Chest. 2013; 143: e93S-e120S
        • Gould M.K.
        • Fletcher J.
        • Iannettoni M.D.
        • et al.
        Evaluation of patients with pulmonary nodules: when is it lung cancer?: ACCP evidence-based clinical practice guidelines (2nd edition).
        Chest. 2007; 132: 108S-130S
        • Heuvelmans M.A.
        • Oudkerk M.
        • de Bock G.H.
        • et al.
        Optimisation of volume-doubling time cutoff for fast-growing lung nodules in CT lung cancer screening reduces false-positive referrals.
        Eur Radiol. 2013; 23: 1836-1845
        • Mehta H.J.
        • Ravenel J.G.
        • Shaftman S.R.
        • et al.
        The utility of nodule volume in the context of malignancy prediction for small pulmonary nodules.
        Chest. 2014; 145: 464-472
        • Goodman L.R.
        • Gulsun M.
        • Washington L.
        • et al.
        Inherent variability of CT lung nodule measurements in vivo using semiautomated volumetric measurements.
        AJR Am J Roentgenol. 2006; 186: 989-994
        • Bogot N.R.
        • Kazerooni E.A.
        • Kelly A.M.
        • et al.
        Interobserver and intraobserver variability in the assessment of pulmonary nodule size on CT using film and computer display methods.
        Acad Radiol. 2005; 12: 948-956
        • Wormanns D.
        • Kohl G.
        • Klotz E.
        • et al.
        Volumetric measurements of pulmonary nodules at multi-row detector CT: in vivo reproducibility.
        Eur Radiol. 2004; 14: 86-92
        • de Hoop B.
        • Gietema H.
        • van de Vorst S.
        • et al.
        Pulmonary ground-glass nodules: increase in mass as an early indicator of growth.
        Radiology. 2010; 255: 199-206
        • Ko J.P.
        • Azour L.
        Management of incidental lung nodules.
        Semin Ultrasound CT MR. 2018; 39: 249-259
        • Travis W.D.
        • Asamura H.
        • Bankier A.A.
        • et al.
        The IASLC lung cancer staging project: proposals for coding T categories for subsolid nodules and assessment of tumor size in part-solid tumors in the forthcoming eighth edition of the TNM classification of lung cancer.
        J Thorac Oncol. 2016; 11: 1204-1223
        • Cho J.Y.
        • Leem C.S.
        • Kim Y.
        • et al.
        Solid part size is an important predictor of nodal metastasis in lung cancer with a subsolid tumor.
        BMC Pulm Med. 2018; 18: 151
        • Kolossvary M.
        • Kellermayer M.
        • Merkely B.
        • et al.
        Cardiac computed tomography radiomics: a comprehensive review on radiomic techniques.
        J Thorac Imaging. 2018; 33: 26-34
        • Doane D.P.
        • Seward L.E.
        Measuring skewness: a forgotten statistic?.
        J Stat Educ. 2011; 19: 18
        • DeCarlo L.T.
        On the meaning and use of kurtosis.
        Psychol Methods. 1997; 2: 292-307
        • Austin J.H.
        • Garg K.
        • Aberle D.
        • et al.
        Radiologic implications of the 2011 classification of adenocarcinoma of the lung.
        Radiology. 2013; 266: 62-71
        • Lim H.J.
        • Ahn S.
        • Lee K.S.
        • et al.
        Persistent pure ground-glass opacity lung nodules ≥ 10 mm in diameter at CT scan: histopathologic comparisons and prognostic implications.
        Chest. 2013; 144: 1291-1299
        • Han L.
        • Zhang P.
        • Wang Y.
        • et al.
        CT quantitative parameters to predict the invasiveness of lung pure ground-glass nodules (pGGNs).
        Clin Radiol. 2018; 73: 504.e1-504.e7
        • Ko J.P.
        • Suh J.
        • Ibidapo O.
        • et al.
        Lung adenocarcinoma: correlation of quantitative CT findings with pathologic findings.
        Radiology. 2016; 280: 931-939
        • Li Q.
        • Fan L.
        • Cao E.T.
        • et al.
        Quantitative CT analysis of pulmonary pure ground-glass nodule predicts histological invasiveness.
        Eur J Radiol. 2017; 89: 67-71
        • Alpert J.B.
        • Rusinek H.
        • Ko J.P.
        • et al.
        Lepidic predominant pulmonary lesions (LPL): CT-based distinction from more invasive adenocarcinomas using 3D volumetric density and first-order CT texture analysis.
        Acad Radiol. 2017; 24: 1604-1611
        • Son J.Y.
        • Lee H.Y.
        • Lee K.S.
        • et al.
        Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.
        PLoS One. 2014; 9: e104066
        • Hwang I.P.
        • Park C.M.
        • Park S.J.
        • et al.
        Persistent pure ground-glass nodules larger than 5 mm: differentiation of invasive pulmonary adenocarcinomas from preinvasive lesions or minimally invasive adenocarcinomas using texture analysis.
        Invest Radiol. 2015; 50: 798-804
        • Chae H.D.
        • Park C.M.
        • Park S.J.
        • et al.
        Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.
        Radiology. 2014; 273: 285-293
        • Kessler L.G.
        • Barnhart H.X.
        • Buckler A.J.
        • et al.
        The emerging science of quantitative imaging biomarkers terminology and definitions for scientific studies and regulatory submissions.
        Stat Methods Med Res. 2015; 24: 9-26
        • Mulshine J.L.
        • Gierada D.S.
        • Armato 3rd, S.G.
        • et al.
        Role of the quantitative imaging biomarker alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules.
        J Am Coll Radiol. 2015; 12: 390-395
        • Athelogou M.
        • Kim H.J.
        • Dima A.
        • et al.
        Algorithm variability in the estimation of lung nodule volume from phantom CT scans: results of the QIBA 3A public challenge.
        Acad Radiol. 2016; 23: 940-952