Risk of malignancy in pulmonary nodules: A validation study of four prediction models.

Abstract

Clinical prediction models assess the likelihood of malignancy in pulmonary nodules detected by computed tomography (CT). This study aimed to validate four such models in a UK population of patients with pulmonary nodules. Three models used clinical and CT characteristics to predict risk (Mayo Clinic, Veterans Association, Brock University) with a fourth model (Herder et al. [4]) additionally incorporating (18)Fluorine-Fluorodeoxyglucose (FDG) avidity on positron emission tomography-computed tomography (PET-CT). The likelihood of malignancy was calculated for patients with pulmonary nodules (4-30mm diameter) and data used to calculate the area under the receiver operating characteristic curve (AUC) for each model. The models were used in a restricted cohort of patients based on each model's exclusion criteria and in the total cohort of all patients. Two hundred and forty-four patients were studied, of whom 139 underwent FDG PET-CT. Ninety-nine (40.6%) patients were subsequently confirmed to have malignant nodules (33.2% primary lung cancer, 7.4% metastatic disease). The Mayo and Brock models performed similarly (AUC 0.895 and 0.902 respectively) and both were significantly better than the Veterans Association model (AUC 0.735, p<0.001 and p=0.002 respectively). In patients undergoing FDG PET-CT, the Herder model had significantly higher accuracy than the other three models (AUC 0.924). When the models were tested on all patients in the cohort (i.e. including those outside the original model inclusion criteria) AUC values were reduced, yet remained high especially for the Herder model (AUC 0.916). For sub-centimetre nodules, AUC values for the Mayo and Brock models were 0.788 and 0.852 respectively. The Mayo and Brock models showed good accuracy for determining likelihood of malignancy in nodules detected on CT scan. In patients undergoing FDG PET-CT for nodule evaluation, the highest accuracy was seen for the model described by Herder et al. incorporating FDG avidity.