Gian Alberto Soardi1, Simone Perandini2, Anna Rita Larici3, Annemilia Del Ciello3, Giovanna Rizzardi4, Antonio Solazzo5, Laura Mancino6, Marco Bernhart7, Massimiliano Motton1, Stefania Montemezzi1. 1. UOC Radiologia, Ospedale Maggiore di Borgo Trento, AOUI Verona, Verona, Italy. 2. UOC Radiologia, Ospedale Maggiore di Borgo Trento, AOUI Verona, Verona, Italy. mail@simoneperandini.com. 3. Dipartimento di Scienze Radiologiche, Università Cattolica del Sacro Cuore, Roma, Italy. 4. UO Chirurgia Toracica, Ospedale Humanitas Gavazzeni, Bergamo, Italy. 5. UO Radiologia, Ospedale Humanitas Gavazzeni, Bergamo, Italy. 6. UO Pneumologia, Ospedale dell'Angelo di Mestre (VE), Mestre, Italy. 7. UO Radiologia, Ospedale dell'Angelo di Mestre (VE), Mestre, Italy.
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.
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.
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