Onno M Mets MD, Presenter: Nothing to Disclose

C. F. M. Buckens MD, Abstract Co-Author: Nothing to Disclose

Pieter Zanen MD, PhD, Abstract Co-Author: Nothing to Disclose

Ivana Isgum PhD, Abstract Co-Author: Nothing to Disclose

Mathias Prokop MD, PhD, Abstract Co-Author: Nothing to Disclose

Pim A. De Jong MD, PhD, Abstract Co-Author: Nothing to Disclose

To develop a CT based diagnostic model for the presence of airflow limitation in current or former heavy smokers participating in a lung cancer screening study.

In 1173 screening participants, we analyzed paired inspiratory and expiratory chest computed tomography (CT) scans, patient characteristics, smoking history and prebronchodilator spirometry. CT scans and pulmonary function tests were obtained at the same day. Scan parameters were adjusted to body weight: 120kVp (≤80kg) or 140kVp (>80kg) both at 30mAs for inspiratory scans, and 90kVp (≤80kg) or 120kVp (>80kg) both at 20mAs for expiratory scans. Axial slices of 1mm at 0.7mm increment were reconstructed. Fully automatic analysis was used to quantify lung density. CT emphysema was defined as percentage of voxels <-950 Hounsfield Unit (HU). CT air trapping was defined as the expiratory to inspiratory ratio of mean lung density. Airflow limitation was defined as FEV1/FVC< 0.70 (forced expiratory volume in one second over forced vital capacity ratio). Logistic regression was used to fit a prediction model; internal validation was performed by bootstrapping to correct for over-optimism.

In our population, 38% (450/1173) of the subjects had airflow limitation. Our final model included CT emphysema, CT air trapping, body mass index (BMI), packyears and smoking status. After internal validation the area under the receiver operating characteristic (ROC) curve was 0.84. The optimal point at the ROC curve was at sensitivity 0.62 and specificity 0.88; this corresponds to 62% (280/450) of the subjects with airflow limitation being identified, at the cost of only 24% (90/370) false positives.

Fully automatic quantitative measures of emphysema and air trapping in CT scans, combined with simple patient characteristics, can identify the majority of subjects suffering from airflow limitation.

Our model identifies heavy smokers with airflow limitation, based on radiological data. This is additional information that offers the possibility of focused lung function evaluation and intervention.

Mets, O, Buckens, C, Zanen, P, Isgum, I, Prokop, M, De Jong, P, Development and Validation of a Diagnostic Model for Airflow Limitation in Heavy Smokers by Using Quantitative Computed Tomography.  Radiological Society of North America 2011 Scientific Assembly and Annual Meeting, November 26 - December 2, 2011 ,Chicago IL. http://archive.rsna.org/2011/11034232.html