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Validation of Two Models to Estimate the Probability of Malignancy in Patients with Solitary Pulmonary Nodules
  1. Ellen M Schultz (emschultz{at}stanford.edu)
  1. Stanford University, United States
    1. Gillian D Sanders (gillian.sanders{at}duke.edu)
    1. Duke University, United States
      1. Priscilla R Trotter (priscilla.trotter{at}duke.edu)
      1. Duke University, United States
        1. Edward F Patz, Jr. (patz0002{at}mc.duke.edu)
        1. Duke University, United States
          1. Gerard A Silvestri (silvestr{at}musc.edu)
          1. Medical University of South Carolina, United States
            1. Douglas K Owens (owens{at}stanford.edu)
            1. VA Palo Alto; Stanford University, United States
              1. Michael K Gould (gould{at}stanford.edu)
              1. VA Palo Alto; Stanford University School of Medicine, United States

                Abstract

                Background: Effective strategies for managing patients with solitary pulmonary nodules (SPN) depend critically on the pre-test probability of malignancy.

                Objective: To validate two previously developed models that estimate the probability that an indeterminate solitary pulmonary nodule (SPN) is malignant, based on clinical characteristics and radiographic findings.

                Methods: We retrospectively collected data on age, smoking and cancer history, nodule size, location, and spiculation from the medical records of 151 veterans (145 men, 6 women; range 39 to 87 years) with an SPN measuring 7 to 30 mm (inclusive) and a final diagnosis established by histopathology or 2-year follow-up. We compared each patient's final diagnosis to the probability of malignancy predicted by two models: one developed by investigators at the Mayo Clinic and another that we developed from patients enrolled in a VA Cooperative Study. We assessed model accuracy by calculating areas under the receiver operating characteristic (ROC) curve and model calibration by comparing predicted and observed rates of malignancy.

                Results: The area under the ROC curve for the Mayo Clinic model (0.80; 95% CI 0.72-0.88) was higher than that of the VA model (0.73; 95% CI 0.64-0.82), but this difference was not statistically significant (Δ=0.07; 95% CI -0.03 to 0.16). Calibration curves showed that the probability of malignancy was underestimated by the Mayo Clinic model and overestimated by the VA model.

                Conclusions: Two existing prediction models are sufficiently accurate to guide decisions about the selection and interpretation of subsequent diagnostic tests in patients with SPNs, although clinicians should also consider the prevalence of malignancy in their practice setting when choosing a model.

                • Coin Lesion, pulmonary
                • Diagnosis
                • Lung Neoplasms
                • Models, statistical
                • Receiver Operating Characteristic (ROC) curve

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