Abstract
Objectives
The aim of this study was to compare classification results from four major risk prediction models in a wide population of incidentally detected solitary pulmonary nodules (SPNs) which were selected to crossmatch inclusion criteria for the selected models.
Methods
A total of 285 solitary pulmonary nodules with a definitive diagnosis were evaluated by means of four major risk assessment models developed from non-screening populations, namely the Mayo, Gurney, PKUPH and BIMC models. Accuracy was evaluated by receiver operating characteristic (ROC) area under the curve (AUC) analysis. Each model’s fitness to provide reliable help in decision analysis was primarily assessed by adopting a surgical threshold of 65 % and an observation threshold of 5 % as suggested by ACCP guidelines.
Results
ROC AUC values, false positives, false negatives and indeterminate nodules were respectively 0.775, 3, 8, 227 (Mayo); 0.794, 41, 6, 125 (Gurney); 0.889, 42, 0, 144 (PKUPH); 0.898, 16, 0, 118 (BIMC).
Conclusions
Resultant data suggests that the BIMC model may be of greater help than Mayo, Gurney and PKUPH models in preoperative SPN characterization when using ACCP risk thresholds because of overall better accuracy and smaller numbers of indeterminate nodules and false positive results.
Key Points
• The BIMC and PKUPH models offer better characterization than older prediction models
• Both the PKUPH and BIMC models completely avoided false negative results
• The Mayo model suffers from a large number of indeterminate results
Similar content being viewed by others
References
Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722
Ost D, Fein AM, Feinsilver SH, Clinical practice (2003) The solitary pulmonary nodule. N Engl J Med 348:2535–2542
Zhao SW, Zhao T, Zhang EH (2004) Evaluation of isolated pulmonary nodules: a comparison of HRCT with conventional CT. J Med Imaging 14:806–808
Gould MK, Donington J, Lynch WR, Mazzone PJ, Midthun DE, Naidich DP et al (2013) Evaluation of individuals with pulmonary nodules: when is it lung cancer? Diagnosis and management of lung cancer, American College of Chest Physicians evidence-based clinical practice guidelines. Chest 143(5 Suppl):93S–120S
Swensen SJ, Silverstein MD, Ilstrup DM, Schleck CD, Edell ES (1997) The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med 157:849–855
Gurney JW, Lyddon DM, McKay JA (1993) Determining the likelihood of malignancy in solitary pulmonary nodules with Bayesian analysis. Part II. Application. Radiology 186:415–422
Li Y, Wang J (2012) A mathematical model for predicting malignancy of solitary pulmonary nodules. World J Surg 36:830–835
Soardi GA, Perandini S, Motton M, Montemezzi S (2015) Assessing probability of malignancy in solid solitary pulmonary nodules with a new Bayesian calculator: improving diagnostic accuracy by means of expanded and updated features. Eur Radiol 25:155–162
Isbell JM, Deppen S, Putnam JB Jr, Nesbitt JC, Lambright ES, Dawes A et al (2011) Existing general population models inaccurately predict lung cancer risk in patients referred for surgical evaluation. Ann Thorac Surg 91:227–233
Perandini S, Soardi GA, Motton M, Dallaserra C, Montemezzi S (2014) Limited value of logistic regression analysis in solid solitary pulmonary nodules characterization: a single-centre experience on 288 consecutive cases. J Surg Oncol 110:883–887
Wang Y-XJ, Gong J-S, Suzuki K, Morcos SK (2014) Evidence based imaging strategies for solitary pulmonary nodule. J Thorac Dis 6(7):872–887
MacMahon H, Austin JH, Gamsu G, Herold CJ, Jett JR, Naidich DP et al (2005) Guidelines for management of small pulmonary nodules detected on CT scans: a statement from the Fleischner Society. Radiology 237:395–400
Callister MEJ, Baldwin DR, Akram AR, Barnard S, Cane P, Draffan J et al (2015) British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 70:ii1–ii54
Louie AV, Senan S, Patel P et al (2014) When is a biopsy-proven diagnosis necessary before stereotactic ablative radiotherapy for lung cancer? A decision analysis. Chest 146:1021–1028
Xiao F, Liu D, Guo Y, Shi B, Song Z, Tian Y et al (2013) Novel and convenient method to evaluate the character of solitary pulmonary nodule – comparison of three mathematical prediction models and further stratification of risk factors. PLoS One 8(10), e78271
Gould MK, Ananth L, Barnett PG (2007) A clinical model to estimate the pretest probability of lung cancer in patients with solitary pulmonary nodules. Chest 131(2):383–388
Schultz EM, Sanders GD, Trotter PR, Patz EF Jr, Silvestri GA, Owens DK et al (2008) Validation of two models to estimate the probability of malignancy in patients with solitary pulmonary nodules. Thorax 63:335–341
Al-Ameri A, Malhotra P, Thygesen H, Plant PK, Vaidyanathan S, Karthik S et al (2015) Risk of malignancy in pulmonary nodules: a validation study of four prediction models. Lung Cancer 89(1):27–30
McWilliams A, Tammemagi C, Mayo J, Roberts H, Liu G, Soghrati K et al (2013) Probability of cancer in pulmonary nodules detected on first screening CT. N Eng J Med 369:910–919
Perandini S, Soardi GA, Motton M, Montemezzi S (2015) Critique of Al-Ameri et al. Risk of malignancy in pulmonary nodules: a validation study of four prediction models. Lung Cancer. doi:10.1016/j.lungcan.2015.05.015
Acknowledgments
The scientific guarantor of this publication is Simone Perandini. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors has significant statistical expertise (S.P.). Institutional review board approval was not required because the research involved collection and analysis of existing data. Data and diagnostic specimens were recorded by the investigator in such a manner that subjects cannot be identified. Written informed consent was not required for this study because the research involved collection and analysis of existing data. Data and diagnostic specimens were recorded by the investigator in such a manner that subjects cannot be identified. Methodology: retrospective, diagnostic or prognostic study, performed at one institution.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Perandini, S., Soardi, G.A., Motton, M. et al. Solid pulmonary nodule risk assessment and decision analysis: comparison of four prediction models in 285 cases. Eur Radiol 26, 3071–3076 (2016). https://doi.org/10.1007/s00330-015-4138-9
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-015-4138-9