Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction
Multicentre external validation of the BIMC model for solid solitary pulmonary nodule malignancy prediction
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DOI：
10.1007/s00330-016-4538-5
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发表时间：
2017-05-01
影响因子：
5.9
通讯作者：
Montemezzi, Stefania
中科院分区：
文献类型：
--
作者：
Soardi, Gian Alberto;Perandini, Simone;Montemezzi, Stefania
通过评估临床收集的孤立性肺结节（spn）队列的诊断准确性，为贝叶斯推断恶性肿瘤计算器（BIMC）模型提供多中心外部验证。评估模型对SPN决策分析的影响，并与通过梅奥诊所模型获得的结果进行比较。回顾性收集来自三个中心的200例患者的临床和影像学资料。通过受试者工作特征（ROC）曲线下面积（auc）来评估准确性。采用美国胸科医师学会（ACCP）和英国胸科学会（BTS）风险阈值进行决策分析。ROC分析显示，BIMC模型的AUC为0.880 (95% CI, 0.832-0.928)， Mayo Clinic模型的AUC为0.604 （95% CI, 0.524-0.683）。差异为0.276 （95% CI, 0.190 ~ 0.363, P < 0.0001）。决策分析显示，当使用ACCP风险阈值时，假阴性和假阳性结果的数量略有减少。BIMC模型被证明是表征spn的准确工具。在临床环境中，通过采用当前的ACCP或BTS风险阈值，并在检查过程中指导针对病变的诊断和介入程序，它可以以最小的错误区分恶性结节和良性结节。在这项多中心研究中，BIMC模型的ROC AUC为0.880，与梅奥诊所模型相比，BIMC模型具有较好的优势。
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.aEuro cent The BIMC model can accurately discriminate malignancies in the clinical settingaEuro cent The BIMC model showed ROC AUC of 0.880 in this multicentre studyaEuro cent The BIMC model compares favourably with the Mayo Clinic model.