We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction.
- Authors
Soardi, Gian; Perandini, Simone; Larici, Anna; Ciello, Annemilia; Rizzardi, Giovanna; Solazzo, Antonio; Mancino, Laura; Bernhart, Marco; Motton, Massimiliano; Montemezzi, Stefania; Soardi, Gian Alberto; Larici, Anna Rita; Del Ciello, Annemilia
- Abstract
<bold>Objectives: </bold>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.<bold>Methods: </bold>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.<bold>Results: </bold>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.<bold>Conclusions: </bold>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.<bold>Key Points: </bold>• 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.
- Subjects
LUNG cancer diagnosis; LUNG cancer treatment; DECISION making; COMPUTED tomography; AMERICAN College of Chest Physicians
- Publication
European Radiology, 2017, Vol 27, Issue 5, p1929
- ISSN
0938-7994
- Publication type
journal article
- DOI
10.1007/s00330-016-4538-5