Word count Abstract: 249 2 Word count manuscript: 3378 Abstract Background Estimation of the clinical probability of malignancy in patients with pulmonary nodules will facilitate early diagnosis, determine optimum patient management strategies and reduce overall costs.Method Data from the UK Lung Cancer Screening (UKLS) trial were analysed. Multivariable logistic regression models were utilised to identify independent predictors and to develop a parsimonious model to estimate the probability of lung cancer in lung nodules detected at baseline, three month and twelve month repeat screening.
ResultsOf 1994 participants that underwent CT scan, 1013 participants had a total of 5063 lung nodules and 52 (2.6%) of the participants developed lung cancer during a median followup of 4 years. Covariates that predict lung cancer in our model 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 (pure ground glass and part solid) and volume as measured by semi-automated volumetry. The final model incorporating all predictors had excellent discrimination; area under the receiver-operating characteristic curve (AUC [95% CI] = 0.885 [0.880 to 0.889]). Internal validation suggested 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) 8.13, P = 0.42).Conclusions Our model may be utilised in estimating the probability of lung cancer in nodules detected at baseline, and three months and twelve months from baseline, allowing more efficient stratification of follow-up in population-based lung cancer screening programs.3
Key Messages• What is the key question?To develop a lung cancer pulmonary nodule risk model which incorporates volumetric measurements.• What is the bottom line?The UKLS pulmonary risk model has excellent discrimination; area under the receiveroperating characteristic curve (AUC [95% CI] = 0.885 [0.880 to 0.889] and has good calibration (goodness-of-fit χ(8) 8.13, P = 0.42).• Why read on?The potential for the UKLS Nodule Risk Model (UKLS-NRM) is that it may be utilised in future national CT screening programmes, which incorporates volumetric measurements to identify malignant pulmonary nodules.