P1.11-32 The UKLS Nodule Risk Model (UKLS-NRM): Utilising Nodule Volumetry

Estimating the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs. Currently there are two risk prediction models, which are recommended by BTS; the Brock University model, for nodules ≥300mm3 or ≥8mm diameter, and where the risk is estimated at >10%, the Herder model after PET-CT. However, none of these models employ volumetry and all were developed for use at baseline The UK Lung Cancer Screening (UKLS) trial data were analysed, utilising multivariable logistic regression models to identify independent predictors and develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline, three month and twelve months repeat screening. 1994 UKLS participants had a CT scan; 1013 had a total of 5063 lung nodules and 52 (2.6%) developed lung cancer during a 4 year median follow-up. Covariates that predict lung cancer included: female gender, asthma, bronchitis, asbestos exposure, history of previous cancer, early and late onset of family history of lung cancer, smoking duration, forced vital capacity, nodule type and volume. The final model had excellent discrimination; area under the receiver-operating characteristic curve (AUC [95% CI] = 0.885 [0.880 to 0.889]). Internal validation indicated that the model will discriminate well when applied to new data (optimism-corrected AUC = 0.882 [0.848-.907]). The risk model had a good calibration (goodness-of-fit χ 8.13, P = 0.42). The UKLS Nodule Risk Model (UKLS-NRM) estimates the probability of lung cancer in nodules detected at baseline, three months and twelve months from baseline. The model is based on readily available, strong, and plausible covariates that have been implicated in the aetiology of lung cancer. The application of UKLS-NRM has the potential to be used in both the research and clinical setting.