Objective: In lung cancer screening practice low‐dose computed tomography, diameter, and volumetric measurement have been used in the management of screen‐detected lung nodules. The aim of this study was to compare the performance of nodule malignancy risk prediction tools using diameter or volume and between computer‐aided detection (CAD) and radiologist measurements. Methods: Multivariable logistic regression models were prepared by using data from two multicenter lung cancer screening trials. For model development and validation, baseline low‐dose computed tomography scans from the Pan‐Canadian Early Detection of Lung Cancer Study and a subset of National Lung Screening Trial (NLST) scans with lung nodules 3 mm or more in mean diameter were analyzed by using the CIRRUS Lung Screening Workstation (Radboud University Medical Center, Nijmegen, the Netherlands). In the NLST sample, nodules with cancer had been matched on the basis of size to nodules without cancer. Results: Both CAD‐based mean diameter and volume models showed excellent discrimination and calibration, with similar areas under the receiver operating characteristic curves of 0.947. The two CAD models had predictive performance similar to that of the radiologist‐based model. In the NLST validation data, the CAD mean diameter and volume models also demonstrated excellent discrimination: areas under the curve of 0.810 and 0.821, respectively. These performance statistics are similar to those of the Pan‐Canadian Early Detection of Lung Cancer Study malignancy probability model with use of these data and radiologist‐measured maximum diameter. Conclusion: Either CAD‐based nodule diameter or volume can be used to assist in predicting a nodule's malignancy risk.

中文翻译：

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预测筛查检测到的肺结节的恶性肿瘤风险 - 平均直径或体积

目的：在肺癌筛查实践中，低剂量计算机断层扫描、直径和体积测量已用于管理筛查检测到的肺结节。本研究的目的是比较结节恶性风险预测工具使用直径或体积以及计算机辅助检测 (CAD) 和放射科测量的性能。方法：使用来自两个多中心肺癌筛查试验的数据建立多变量逻辑回归模型。对于模型开发和验证，来自泛加拿大肺癌早期检测研究的基线低剂量计算机断层扫描和平均直径为 3 毫米或更大的肺结节的国家肺筛查试验 (NLST) 扫描的一个子集通过使用进行了分析CIRRUS 肺筛查工作站（Radboud 大学医学中心，奈梅亨，荷兰人）。在 NLST 样本中，有癌症的结节已根据大小与没有癌症的结节相匹配。结果：基于 CAD 的平均直径和体积模型均显示出出色的辨别力和校准能力，接收器操作特征曲线下的面积相似，为 0.947。这两个 CAD 模型的预测性能与基于放射科医师的模型相似。在 NLST 验证数据中，CAD 平均直径和体积模型也表现出极好的辨别力：曲线下面积分别为 0.810 和 0.821。这些性能统计数据类似于使用这些数据和放射科医生测量的最大直径的泛加拿大肺癌早期检测研究恶性肿瘤概率模型的统计数据。结论：