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

Anna Rita Larici, Annemilia Del Ciello, Gian Alberto Soardi, Simone Perandini, Giovanna Rizzardi, Antonio Solazzo, Laura Mancino, Marco Bernhart, Massimiliano Motton, Stefania Montemezzi

Risultato della ricerca: Contributo in rivistaArticolo in rivista

13 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)1929-1933
Numero di pagine5
RivistaEuropean Radiology
Volume27
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Aged
  • Clinical Decision-Making
  • Computed tomography
  • Decision Support Techniques
  • Decision analysis
  • Early Detection of Cancer
  • Epidemiologic Methods
  • Female
  • Humans
  • Lung Neoplasms
  • Lung cancer
  • Male
  • Models, Theoretical
  • Solid pulmonary nodule
  • Solitary Pulmonary Nodule

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