Automated Computed Tomography analysis in the assessment of Idiopathic Pulmonary Fibrosis severity and progression

Published:January 28, 2020DOI:


      • CALIPER distinguishes between treated and untreated patients during follow-up.
      • CALIPER correlates with pulmonary function tests at baseline and during follow-up.
      • CALIPER correlates with prognosis in idiopathic pulmonary fibrosis patients.



      To investigate the role of a quantitative analysis software (CALIPER) in identifying HRCT thresholds predicting IPF patients’ survival and lung function decline and its role in detecting changes of HRCT abnormalities related to treatment and their correlation with Forced Vital Capacity (FVC).


      This retrospective study included 105 patients with a multidisciplinary diagnosis of IPF for whom one HRCT at baseline and concomitant FVC were available.
      HRCTs were evaluated with CALIPER and the correlation between FVC and radiological features were assessed. Radiological thresholds for survival prediction and functional decline were calculated for all patients. Fifty-nine patients with at least 2 serial HRCTs were classified into two groups based on treatment. For patients for whom a FVC within 3 months of the HRCT was available (n = 44), the correlation of radiological and clinical progression was evaluated.


      The correlation between FVC and CALIPER-derived features at baseline was significant and strong. A baseline CALIPER-derived interstitial lung disease (ILD%) extent higher than 20 % and pulmonary vascular related structures (PVRS%) score greater than 5 % defined a worse prognosis.
      A significant progression of CALIPER-derived features in all patients was found with a faster increase in untreated patients. ILD% and PVRS% changes during follow-up demonstrated strong correlations with FVC changes.


      CALIPER quantification of fibrosis and vascular involvement could distinguish disease progression in treated versus untreated patients and predict the survival. The changes in CALIPER-derived variables over time were significantly correlated to changes in FVC.


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


        • Raghu G.
        • Collard H.R.
        • Egan J.J.
        • et al.
        An official ATS/ERS/JRS/ALAT statement: idiopathic pulmonary fibrosis: evidence-based guidelines for diagnosis and management.
        Am. J. Respir. Crit. Care Med. 2011; 183: 788-824
        • Kim H.J.
        • Perlman D.
        • Tomic R.
        Natural history of idiopathic pulmonary fibrosis.
        Respir. Med. 2015; 109: 661-670
        • Raghu G.
        • Rochwerg B.
        • Zhang Y.
        • et al.
        An official ATS/ERS/JRS/ALAT clinical practice guideline: treatment of idiopathic pulmonary fibrosis. An update of the 2011 clinical practice guideline.
        Am. J. Respir. Crit. Care Med. 2015; 192: e3-e19
        • Travis W.D.
        • Costabel U.
        • Hansell D.M.
        • et al.
        An official American Thoracic Society/European Respiratory Society statement: update of the international multidisciplinary classification of the idiopathic interstitial pneumonias.
        Am. J. Respir. Crit. Care Med. 2013; 188: 733-748
        • Hansell D.M.
        • Goldin J.G.
        • King T.E.
        • et al.
        CT staging and monitoring of fibrotic interstitial lung diseases in clinical practice and treatment trials: a position paper from the Fleischner Society.
        Lancet Respir. Med. 2015; 3: 483-496
        • Walsh S.L.
        • Calandriello L.
        • Sverzellati N.
        • et al.
        Interobserver agreement for the ATS/ERS/JRS/ALAT criteria for a UIP pattern on CT.
        Thorax. 2016; 71: 45-51
        • Maldonado F.
        • Moua T.
        • Rajagopalan S.
        • et al.
        Automated quantification of radiological patterns predicts survival in idiopathic pulmonary fibrosis.
        Eur. Respir. J. 2014; 43: 204-212
        • Jacob J.
        • Bartholmai B.J.
        • Rajagopalan S.
        • et al.
        Automated quantitative computed tomography versus visual computed tomography scoring in idiopathic pulmonary fibrosis: validation against pulmonary function.
        J. Thorac. Imaging. 2016; 31: 304-311
        • Jacob J.
        • Bartholmai B.J.
        • Rajagopalan S.
        • et al.
        Mortality prediction in idiopathic pulmonary fibrosis: evaluation of computer-based CT analysis with conventional severity measures.
        Eur. Respir. J. 2017; : 49
        • Miller M.R.
        • Hankinson J.
        • Brusasco V.
        • et al.
        Standardisation of spirometry.
        Eur. Respir. J. 2005; 26: 319-338
        • Park H.J.
        • Lee S.M.
        • Song J.W.
        • et al.
        Texture-based automated quantitative assessment of regional patterns on initial CT in patients with idiopathic pulmonary fibrosis: relationship to decline in forced vital capacity.
        AJR Am. J. Roentgenol. 2016; 207: 976-983
        • Zavaletta V.A.
        • Bartholmai B.J.
        • Robb R.A.
        High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis.
        Acad. Radiol. 2007; 14: 772-787
        • Karwoski Ronald A.
        • Bartholmai B.
        • Zavaletta Vanessa A.
        • et al.
        Processing of CT images for analysis of diffuse lung disease in the lung tissue research consortium, Vortrag.
        SPIE Proceedings. 2008; (SPIE. S. 691614)
        • Best A.C.
        • Meng J.
        • Lynch A.M.
        • et al.
        Idiopathic pulmonary fibrosis: physiologic tests, quantitative CT indexes, and CT visual scores as predictors of mortality.
        Radiology. 2008; 246: 935-940
        • Sumikawa H.
        • Johkoh T.
        • Colby T.V.
        • et al.
        Computed tomography findings in pathological usual interstitial pneumonia: relationship to survival.
        Am. J. Respir. Crit. Care Med. 2008; 177: 433-439
        • Wu X.
        • Kim G.H.
        • Salisbury M.L.
        • et al.
        Computed tomographic biomarkers in idiopathic pulmonary fibrosis. The future of quantitative analysis.
        Am. J. Respir. Crit. Care Med. 2019; 199: 12-21
        • Goldin J.G.
        • Kim G.H.J.
        • Tseng C.H.
        • et al.
        Longitudinal changes in quantitative interstitial lung disease on computed tomography after immunosuppression in the scleroderma lung study II.
        Ann. Am. Thorac. Soc. 2018; 15: 1286-1295
        • Walsh S.L.F.
        • Calandriello L.
        • Silva M.
        • Sverzellati N.
        Deep learning for classifying fibrotic lung disease on high-resolution computed tomography: a case-cohort study.
        Lancet Respir. Med. 2018; 6: 837-845
        • Jacob J.
        • Bartholmai B.J.
        • Rajagopalan S.
        • et al.
        Serial automated quantitative CT analysis in idiopathic pulmonary fibrosis: functional correlations and comparison with changes in visual CT scores.
        Eur. Radiol. 2018; 28: 1318-1327
        • Lee S.M.
        • Seo J.B.
        • Oh S.Y.
        • et al.
        Prediction of survival by texture-based automated quantitative assessment of regional disease patterns on CT in idiopathic pulmonary fibrosis.
        Eur. Radiol. 2018; 28: 1293-1300
        • Jacob J.
        • Bartholmai B.J.
        • Rajagopalan S.
        • et al.
        Predicting outcomes in idiopathic pulmonary fibrosis using automated computed tomographic analysis.
        Am. J. Respir. Crit. Care Med. 2018; 198: 767-776
        • Iwasawa T.
        • Ogura T.
        • Sakai F.
        • et al.
        CT analysis of the effect of pirfenidone in patients with idiopathic pulmonary fibrosis.
        Eur. J. Radiol. 2014; 83: 32-38