title: Using Clinical Risk Models for Lung Nodule Classification.
abstract: Evaluation and diagnosis of indeterminate pulmonary nodules is a significant and increasing burden on our health care system. The advent of lung cancer screening with low-dose computed tomography only exacerbates this problem, and more surgeons will be evaluating smaller and screening discovered nodules. Multiple calculators exist that can help the clinician diagnose lung cancer at the bedside. The Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) model helps to determine who needs lung cancer screening, and the McWilliams and Mayo models help to guide the primary care clinician or pulmonologist with diagnosis by estimating the probability of cancer in patients with indeterminate pulmonary nodules. The Thoracic Research Evaluation And Treatment (TREAT) model assists surgeons to determine who needs a surgical biopsy among patients referred for suspicious lesions. Additional work is needed to develop decision support tools that will facilitate the use of these models in clinical practice, to complement the clinician's judgment and enhance shared decision making with the patient at the bedside.
authors.affiliation: Department of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, Tennessee; Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.
authors.forename: Stephen A
authors.initials: SA
authors.lastname: Deppen
authors.affiliation: Department of Surgery, Tennessee Valley Healthcare System, Veterans Affairs, Nashville, Tennessee; Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee. Electronic address: eric.grogan@vanderbilt.edu.
authors.forename: Eric L
authors.initials: EL
authors.lastname: Grogan
mesh_terms: D006801:Humans; D008175:Lung Neoplasms / Q000145:classification*; D055613:Multiple Pulmonary Nodules / Q000145:classification*; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors
keywords: clinical models; lung cancer screening; lung nodule; prediction