Abstract
Objectives
To provide multicentre external validation of the Bayesian Inference Malignancy Calculator (BIMC) model by assessing diagnostic accuracy in a cohort of solitary pulmonary nodules (SPNs) collected in a clinic-based setting. To assess model impact on SPN decision analysis and to compare findings with those obtained via the Mayo Clinic model.
Methods
Clinical and imaging data were retrospectively collected from 200 patients from three centres. Accuracy was assessed by means of receiver-operating characteristic (ROC) areas under the curve (AUCs). Decision analysis was performed by adopting both the American College of Chest Physicians (ACCP) and the British Thoracic Society (BTS) risk thresholds.
Results
ROC analysis showed an AUC of 0.880 (95 % CI, 0.832-0.928) for the BIMC model and of 0.604 (95 % CI, 0.524-0.683) for the Mayo Clinic model. Difference was 0.276 (95 % CI, 0.190-0.363, P < 0.0001). Decision analysis showed a slightly reduced number of false-negative and false-positive results when using ACCP risk thresholds.
Conclusions
The BIMC model proved to be an accurate tool when characterising SPNs. In a clinical setting it can distinguish malignancies from benign nodules with minimal errors by adopting current ACCP or BTS risk thresholds and guiding lesion-tailored diagnostic and interventional procedures during the work-up.
Key Points
• The BIMC model can accurately discriminate malignancies in the clinical setting
• The BIMC model showed ROC AUC of 0.880 in this multicentre study
• The BIMC model compares favourably with the Mayo Clinic model
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ost D, Fein AM, Feinsilver SH (2007) The solitary pulmonary nodule. N Engl J Med 348:2535–2542
Gould MK, Donington J, Lynch WR et al (2013) Evaluation of individuals with pulmonary nodules: when is it lung cancer?: Diagnosis and management of lung cancer, 3rd ed: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines. Chest 143:e93S–e120S
Callister ME, Baldwin DR, Akram AR et al (2015) British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 70:ii1–ii54
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
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
Swensen SJ, Silverstein MD, Ilstrup DM et al (1997) The probability of malignancy in solitary pulmonary nodules. Application to small radiologically indeterminate nodules. Arch Intern Med 157:849–855
Herder GJ, van Tinteren H, Golding RP et al (2005) Clinical prediction model to characterize pulmonary nodules: validation and added value of 18F-fluorodeoxyglucose positron emission tomography. Chest 128:2490–2496
Al-Ameri A, Malhotra P, Thygesen H et al (2015) Risk of malignancy in pulmonary nodules: a validation study of four prediction models. Lung Cancer 89:27–30
Isbell JM, Deppen S, Putnam JB Jr 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-center experience on 288 consecutive cases. J Surg Oncol 110:883–887
Perandini S, Soardi GA, Motton M, Montemezzi S (2015) Critique of Al-Ameri et al. (2015) - Risk of malignancy in pulmonary nodules: a validation study of four prediction models. Lung Cancer 90:118–119
Acknowledgments
The authors would like to thank Prof. MC Tammemägi for his valuable suggestions during the writing of this paper and L Brandon for language editing. 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, multicentre study. One hundred fifty-three SPNs are part of a larger data set, which was analysed in a manuscript investigating SPN 18-FDG-PET characterisation and is currently under review.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Soardi, G.A., Perandini, S., Larici, A.R. et al. Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction. Eur Radiol 27, 1929–1933 (2017). https://doi.org/10.1007/s00330-016-4538-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00330-016-4538-5