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
  • 276 Downloads

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

Keywords

Lung cancer Solid pulmonary nodule Decision analysis Computed tomography 

Notes

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.

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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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