Predicting all-cause and lung cancer mortality using emphysema score progression rate between baseline and follow-up chest CT images: A comparison of risk model performances

Purpose Normalized emphysema score is a protocol-robust CT biomarker of mortality. We aimed to improve mortality prediction by including the emphysema score progression rate–its change over time–into the models. Method and materials CT scans from 6000 National Lung Screening Trial CT arm participants were included. Of these, 1810 died (445 lung cancer-specific). The remaining 4190 survivors were sampled with replacement up to 24432 to approximate the full cohort. Three overlapping subcohorts were formed which required participants to have images from specific screening rounds. Emphysema scores were obtained after resampling, normalization, and bullae cluster analysis of the original images. Base models contained solely the latest emphysema score. Progression models included emphysema score progression rate. Models were adjusted by including baseline age, sex, BMI, smoking status, smoking intensity, smoking duration, and previous COPD diagnosis. Cox proportional hazard models predicting all-cause and lung cancer mortality were compared by calculating the area under the curve per year follow-up. Results In the subcohort of participants with baseline and first annual follow-up scans, the analysis was performed on 4940 participants (23227 after resampling). Area under the curve for all-cause mortality predictions of the base and progression models 6 years after baseline were 0.564 (0.564 to 0.565) and 0.569 (0.568 to 0.569) when unadjusted, and 0.704 (0.703 to 0.704) to 0.705 (0.704 to 0.705) when adjusted. The respective performances predicting lung cancer mortality were 0.638 (0.637 to 0.639) and 0.643 (0.642 to 0.644) when unadjusted, and 0.724 (0.723 to 0.725) and 0.725 (0.725 to 0.726) when adjusted. Conclusion Including emphysema score progression rate into risk models shows no clinically relevant improvement in mortality risk prediction. This is because scan normalization does not adjust for an overall change in lung density. Adjusting for changes in smoking behavior is likely required to make this a clinically useful measure of emphysema progression.

[1]  H. Kauczor,et al.  Effect of smoking cessation on quantitative computed tomography in smokers at risk in a lung cancer screening population , 2018, European Radiology.

[2]  Leticia Gallardo-Estrella,et al.  Normalized emphysema scores on low dose CT: Validation as an imaging biomarker for mortality , 2017, PloS one.

[3]  D. Lynch,et al.  Current Smoking Status Is Associated With Lower Quantitative CT Measures of Emphysema and Gas Trapping , 2015, Journal of thoracic imaging.

[4]  Philip F. Judy,et al.  Normalizing computed tomography data reconstructed with different filter kernels: effect on emphysema quantification , 2015, European Radiology.

[5]  C. Berg,et al.  Impact of lung cancer screening results on smoking cessation. , 2014, Journal of the National Cancer Institute.

[6]  Lu Tian,et al.  A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data , 2013, Statistics in medicine.

[7]  David Gur,et al.  Optimal threshold in CT quantification of emphysema , 2012, European Radiology.

[8]  B. van Ginneken,et al.  Quantitative Computed Tomography in COPD: Possibilities and Limitations , 2011, Lung.

[9]  C. Gatsonis,et al.  Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening , 2012 .

[10]  B. Stoel,et al.  Lung densitometry to assess progression of emphysema in chronic obstructive pulmonary disease: time to apply in the clinic? , 2011, American journal of respiratory and critical care medicine.

[11]  B. Stoel,et al.  Rapid Fall in Lung Density Following Smoking Cessation in COPD , 2011, COPD.

[12]  Ewout W Steyerberg,et al.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers , 2011, Statistics in medicine.

[13]  Marleen de Bruijne,et al.  Short-term effect of changes in smoking behaviour on emphysema quantification by CT , 2010, Thorax.

[14]  Judith E. Adams,et al.  Quantitative computed tomography. , 2009, European journal of radiology.

[15]  B. van Ginneken,et al.  Automatic lung segmentation from thoracic computed tomography scans using a hybrid approach with error detection. , 2009, Medical physics.

[16]  A. Gulsvik,et al.  Quantitative computed tomography: emphysema and airway wall thickness by sex, age and smoking , 2009, European Respiratory Journal.

[17]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[18]  Stefano Diciotti,et al.  Prevalence and correlates of pulmonary emphysema in smokers and former smokers. A densitometric study of participants in the ITALUNG trial , 2008, European Radiology.

[19]  Roland Werthschützky,et al.  Automated CT image evaluation of the lung: a morphology-based concept , 2001, IEEE Transactions on Medical Imaging.

[20]  T. Lumley,et al.  Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker , 2000, Biometrics.

[21]  A A Bankier,et al.  Pulmonary emphysema: subjective visual grading versus objective quantification with macroscopic morphometry and thin-section CT densitometry. , 1999, Radiology.

[22]  P De Vuyst,et al.  Comparison of computed density and microscopic morphometry in pulmonary emphysema. , 1996, American journal of respiratory and critical care medicine.

[23]  P De Vuyst,et al.  Comparison of computed density and macroscopic morphometry in pulmonary emphysema. , 1995, American journal of respiratory and critical care medicine.

[24]  N. Müller,et al.  "Density mask". An objective method to quantitate emphysema using computed tomography. , 1988, Chest.

[25]  G. Smirnov,et al.  Possibilities and Limitations , 1970 .

[26]  E. B. Wilson Probable Inference, the Law of Succession, and Statistical Inference , 1927 .