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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, METHODS, RESULTS. Meshes: Adult, Aged, Bayes Theorem, Cohort Studies, Female, Humans, Likelihood Functions, Lung Neoplasms, Male, Middle Aged, Models, Statistical, Predictive Value of Tests, Reproducibility of Results, Retrospective Studies, Risk Factors.
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Do existing general population models inaccurately predict lung cancer risk in patients referred for surgical evaluation? Context: Patients undergoing resections for suspicious pulmonary lesions have a 9% to 55% benign rate. Validated prediction models exist to estimate the probability of malignancy in a general population and current practice guidelines recommend their use. We evaluated these models in a surgical population to determine the accuracy of existing models to predict benign or malignant disease. We conducted a retrospective review of our thoracic surgery quality improvement database (2005 to 2008) to identify patients who underwent resection of a pulmonary lesion. Patients were stratified into subgroups based on age, smoking status, and fluorodeoxyglucose positron emission tomography (PET) results. The probability of malignancy was calculated for each patient using the Mayo and solitary pulmonary nodules prediction models. Receiver operating characteristic and calibration curves were used to measure model performance. A total of 189 patients met selection criteria; 73% were malignant. Patients with preoperative PET scans were divided into four subgroups based on age, smoking history, and nodule PET avidity. Older smokers with PET-avid lesions had a 90% malignancy rate. Patients with PET-nonavid lesions, PET-avid lesions with age less than 50 years, or never smokers of any age had a 62% malignancy rate. The area under the receiver operating characteristic curve for the Mayo and solitary pulmonary nodules models was 0.79 and 0.80, respectively; however, the models were poorly calibrated (p<0.001).
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Despite improvements in diagnostic and imaging techniques, current general population models do not accurately predict lung cancer among patients referred for surgical evaluation. Prediction models with greater accuracy are needed to identify patients with benign disease to reduce nontherapeutic resections. Final Decision: yes