A practical algorithmic approach to the diagnosis and management of solitary pulmonary nodules: part 2: pretest probability and algorithm.

In this second part of a two-part series, we describe an algorithmic approach to the diagnosis of the solitary pulmonary nodule (SPN). An essential aspect of the evaluation of SPN is determining the pretest probability of malignancy, taking into account the significant medical history and social habits of the individual patient, as well as morphologic characteristics of the nodule. Because pretest probability plays an important role in determining the next step in the evaluation, we describe various methods the physician may use to make this determination. Subsequently, we outline a simple yet comprehensive algorithm for diagnosing a SPN, with distinct pathways for the solid and subsolid SPN.

[1]  Peter B Bach,et al.  Screening for lung cancer: ACCP evidence-based clinical practice guidelines (2nd edition). , 2007, Chest.

[2]  N. Müller,et al.  Solitary pulmonary nodule: high-resolution CT and radiologic-pathologic correlation. , 1991, Radiology.

[3]  V. P. Collins,et al.  Differentiation of benign and malignant pulmonary nodules by growth rate. , 1962, Radiology.

[4]  N. Müller,et al.  Lung nodule enhancement at CT: multicenter study. , 2000, Radiology.

[5]  O. Hoekstra,et al.  Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. , 2005, Chest.

[6]  A. Soubani,et al.  The evaluation and management of the solitary pulmonary nodule , 2008, Postgraduate Medical Journal.

[7]  G. Sanders,et al.  Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules , 2007, Thorax.

[8]  H. Soeda,et al.  New Classification of Small Pulmonary Nodules by Margin Characteristics on Highresolution CT , 1999, Acta radiologica.

[9]  A Alperovitch,et al.  Value of computer diagnosis of a single thoracic X-ray circular opacity. , 1974, Biomedicine / [publiee pour l'A.A.I.C.I.G.].

[10]  J L Lehr,et al.  Solitary pulmonary lesions. Computer-aided differential diagnosis and evaluation of mathematical methods. , 1967, Radiology.

[11]  J. Gurney.  Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part I. Theory. , 1993, Radiology.

[12]  S. Friberg,et al.  On the growth rates of human malignant tumors: Implications for medical decision making , 1997, Journal of surgical oncology.

[13]  Walter J. Coulson,et al.  The rate of growth and apparent duration of untreated primary bronchial carcinoma , 1963, Cancer.

[14]  M D Seemann,et al.  Differentiation of malignant from benign solitary pulmonary lesions using chest radiography, spiral CT and HRCT. , 2000, Lung cancer.

[15]  Michael K Gould,et al.  Evidence-Based Clinical Practice Guidelines Nodules : When Is It Lung Cancer ? : ACCP Evaluation of Patients With Pulmonary , 2007 .

[16]  L. Tanoue,et al.  Subsolid Pulmonary Nodules and the Spectrum of Peripheral Adenocarcinomas of the Lung: Recommended Interim Guidelines for Assessment and Management , 2011 .

[17]  M. Gould,et al.  A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. , 2007, Chest.

[18]  S. Raoof,et al.  A practical algorithmic approach to the diagnosis and management of solitary pulmonary nodules: part 1: radiologic characteristics and imaging modalities. , 2013, Chest.

[19]  P. Buell,et al.  Asymptomatic solitary pulmonary nodules. Host survival, tumor size, and growth rate. , 1973, The Journal of thoracic and cardiovascular surgery.

[20]  M. Tsuboi,et al.  Comparison of thin-section CT and pathological findings in small solid-density type pulmonary adenocarcinoma: prognostic factors from CT findings. , 2012, European journal of radiology.

[21]  E A Zerhouni,et al.  CT of the solitary pulmonary nodule. , 1980, AJR. American journal of roentgenology.

[22]  J. Gurney,et al.  Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part II. Application. , 1993, Radiology.

[23]  M. L. R. D. Christenson,et al.  Guidelines for Management of Small Pulmonary Nodules Detected on CT Scans: A Statement From the Fleischner Society , 2006 .

[24]  J Huston,et al.  Solitary pulmonary opacities: plain tomography. , 1987, Radiology.

[25]  W. Weiss.  Tumor doubling time and survival of men with bronchogenic carcinoma. , 1974, Chest.

[26]  S. Swensen,et al.  The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. , 1997, Archives of internal medicine.

[27]  L. Tanoue.  Screening for Lung Cancer: ACCP Evidence-Based Clinical Practice Guidelines (2nd Edition) , 2009 .

[28]  K. Rotte,et al.  Results of computer-aided diagnosis of peripheral bronchial carcinoma. , 1977, Radiology.

[29]  F H Edwards,et al.  Bayesian statistical theory in the preoperative diagnosis of pulmonary lesions. , 1987, Chest.

[30]  R. Bellamy,et al.  Use of artificial intelligence for the preoperative diagnosis of pulmonary lesions. , 1989, The Annals of thoracic surgery.