European Radiology

, Volume 27, Issue 5, pp 1929–1933 | Cite as

Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction

  • Gian Alberto Soardi
  • Simone Perandini
  • Anna Rita Larici
  • Annemilia del Ciello
  • Giovanna Rizzardi
  • Antonio Solazzo
  • Laura Mancino
  • Marco Bernhart
  • Massimiliano Motton
  • Stefania Montemezzi
Computer Applications



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.


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.


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.


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


Lung cancer Solid pulmonary nodule Decision analysis Computed tomography 



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.


  1. 1.
    Ost D, Fein AM, Feinsilver SH (2007) The solitary pulmonary nodule. N Engl J Med 348:2535–2542CrossRefGoogle Scholar
  2. 2.
    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–e120SCrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Callister ME, Baldwin DR, Akram AR et al (2015) British Thoracic Society guidelines for the investigation and management of pulmonary nodules. Thorax 70:ii1–ii54CrossRefPubMedGoogle Scholar
  4. 4.
    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 25:155–162CrossRefPubMedGoogle Scholar
  5. 5.
    Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J (2008) Fleischner Society: glossary of terms for thoracic imaging. Radiology 246:697–722CrossRefPubMedGoogle Scholar
  6. 6.
    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 157:849–855CrossRefPubMedGoogle Scholar
  7. 7.
    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 128:2490–2496CrossRefPubMedGoogle Scholar
  8. 8.
    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 89:27–30CrossRefPubMedGoogle Scholar
  9. 9.
    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 91:227–233CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    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 110:883–887CrossRefPubMedGoogle Scholar
  11. 11.
    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 90:118–119CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Gian Alberto Soardi
    • 1
  • Simone Perandini
    • 1
  • Anna Rita Larici
    • 2
  • Annemilia del Ciello
    • 2
  • Giovanna Rizzardi
    • 3
  • Antonio Solazzo
    • 4
  • Laura Mancino
    • 5
  • Marco Bernhart
    • 6
  • Massimiliano Motton
    • 1
  • Stefania Montemezzi
    • 1
  1. 1.UOC Radiologia, Ospedale Maggiore di Borgo TrentoAOUI VeronaVeronaItaly
  2. 2.Dipartimento di Scienze RadiologicheUniversità Cattolica del Sacro CuoreRomaItaly
  3. 3.UO Chirurgia Toracica, Ospedale Humanitas GavazzeniBergamoItaly
  4. 4.UO Radiologia, Ospedale Humanitas GavazzeniBergamoItaly
  5. 5.UO Pneumologia, Ospedale dell’Angelo di Mestre (VE)MestreItaly
  6. 6.UO Radiologia, Ospedale dell’Angelo di Mestre (VE)MestreItaly

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