A Quantitative Method for Estimating Individual Lung Cancer Risk

Rationale and Objectives: Lung cancer is caused primarily by repeated exposure to carcinogenic particulate matter and noxious gasses with high particulate deposition localized to airway bifurcations and the lung periphery. The quantitative measurement and analysis of these sites has the potential to stratify lung cancer risk. The aim of this preliminary study was to assess the performance of a new method for estimating individual lung cancer risk based on the analysis of airway bifurcations on high-resolution (HR) computed tomographic (CT) scanning and spirometry. Materials and Methods: One hundred eight subjects with spirometry and thin-slice CT data were selected from a CT screening study including 15 patients with early lung cancer and 93 age-matched and pack-year-matched controls. A subset of seven patients with cancer and 72 controls were scanned with 1-mm CT slice thickness, representing an HR case subset. A quantitative lung cancer risk index method was developed on the basis of airway bifurcation x-ray attenuation combined with the ratio of forced expiratory volume in 1 second to forced vital capacity. Cochran-Mantel-Haenszel and conditional logistic regression tests were used to analyze performance. Results: Cochran-Mantel-Haenszel crude analysis revealed a cancer detection sensitivity and specificity of 67% and 72% for all cases and 100% and 73% for the HR case subset, respectively. Conditional logistic regression showed that a 0.0328 increase in lung cancer risk index was associated with odds ratios of 1.84 (95% confidence interval, 1.18-2.85) for the full data set (P = .0067) and 2.89 (95% confidence interval, 1.02-8.19) for the HR subset (P = .0467). Conclusions: A preliminary evaluation of a new lung cancer risk estimation method based on thin slice CT and spirometry showed a statistically significant association with lung cancer.