Perandini Simone, Soardi Gian Alberto, Motton Massimiliano, Augelli Raffaele, Dallaserra Chiara, Puntel Gino, Rossi Arianna, Sala Giuseppe, Signorini Manuel, Spezia Laura, Zamboni Federico, Montemezzi Stefania
Simone Perandini, Gian Alberto Soardi, Massimiliano Motton, Raffaele Augelli, Chiara Dallaserra, Gino Puntel, Arianna Rossi, Giuseppe Sala, Manuel Signorini, Laura Spezia, Federico Zamboni, Stefania Montemezzi, Department of Radiology, Azienda Ospedaliera Universitaria Integrata di Verona, 37100 Verona, Italy.
World J Radiol. 2016 Aug 28;8(8):729-34. doi: 10.4329/wjr.v8.i8.729.
The aim of this study was to prospectively assess the accuracy gain of Bayesian analysis-based computer-aided diagnosis (CAD) vs human judgment alone in characterizing solitary pulmonary nodules (SPNs) at computed tomography (CT). The study included 100 randomly selected SPNs with a definitive diagnosis. Nodule features at first and follow-up CT scans as well as clinical data were evaluated individually on a 1 to 5 points risk chart by 7 radiologists, firstly blinded then aware of Bayesian Inference Malignancy Calculator (BIMC) model predictions. Raters' predictions were evaluated by means of receiver operating characteristic (ROC) curve analysis and decision analysis. Overall ROC area under the curve was 0.758 before and 0.803 after the disclosure of CAD predictions (P = 0.003). A net gain in diagnostic accuracy was found in 6 out of 7 readers. Mean risk class of benign nodules dropped from 2.48 to 2.29, while mean risk class of malignancies rose from 3.66 to 3.92. Awareness of CAD predictions also determined a significant drop on mean indeterminate SPNs (15 vs 23.86 SPNs) and raised the mean number of correct and confident diagnoses (mean 39.57 vs 25.71 SPNs). This study provides evidence supporting the integration of the Bayesian analysis-based BIMC model in SPN characterization.
本研究的目的是前瞻性评估基于贝叶斯分析的计算机辅助诊断（CAD）与单纯人类判断相比，在计算机断层扫描（CT）中对孤立性肺结节（SPN）进行特征描述时的准确性提升情况。该研究纳入了100个经明确诊断的随机选择的SPN。7名放射科医生首先在不了解情况、之后知晓贝叶斯推理恶性肿瘤计算器（BIMC）模型预测结果的情况下，分别在1至5分的风险图表上评估首次及随访CT扫描时的结节特征以及临床数据。通过受试者操作特征（ROC）曲线分析和决策分析来评估评级者的预测。在披露CAD预测结果之前，总体ROC曲线下面积为0.758，之后为0.803（P = 0.003）。7名读者中有6名读者的诊断准确性有净提升。良性结节的平均风险等级从2.48降至2.29，而恶性肿瘤的平均风险等级从3.66升至3.92。知晓CAD预测结果还导致平均不确定SPN数量显著下降（从23.86个降至15个），并提高了正确且有把握的诊断的平均数量（从25.71个升至39.57个）。本研究提供了支持将基于贝叶斯分析的BIMC模型整合到SPN特征描述中的证据。
