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

Published on Jul 1, 2015in Lung Cancer4.702
· DOI :10.1016/J.LUNGCAN.2015.03.018
Ali Al-Ameri1
Estimated H-index: 1
,
P Malhotra4
Estimated H-index: 4
+ 5 AuthorsMatthew E.J. Callister18
Estimated H-index: 18
Sources
Abstract
Abstract Objectives 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 18Fluorine-Fluorodeoxyglucose (FDG) avidity on positron emission tomography–computed tomography (PET–CT). Materials and methods The likelihood of malignancy was calculated for patients with pulmonary nodules (4–30 mm 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. Results 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  Conclusions 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.
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