背景 胸部CT之前的咨询提供了有价值的时间信息（例如，结节大小或外观的变化），以准确估计恶性风险。目的 开发一种深度学习（DL）算法，使用当前和先前的低剂量CT检查来估计肺部结节的3年恶性风险。材料与方法 在这项回顾性研究中，算法使用美国全国肺部筛查试验（National Lung Screening Trial）的数据进行训练（数据采集于2002年至2004年），其中患者之间的成像间隔不超过2年，并且使用了来自丹麦肺癌筛查试验（Danish Lung Cancer Screening Trial）和多中心意大利肺部检测试验（Multicentric Italian Lung Detection Trial）的两个外部测试集，分别在2004年至2010年和2005年至2014年收集。使用肺癌丰富的亚组上的受试者工作特征曲线下面积（AUC）进行性能评估，分别使用DLCST和MILD的1年和2年前成像的大小匹配的良性结节。将算法与经过验证的仅处理单个CT检查的DL算法和Pan-Canadian Early Lung Cancer Detection Study（PanCan）模型进行比较。结果训练集包括10,508个结节（其中422个恶性结节）和4902个试验参与者（平均年龄为64岁±5 [SD]；2778男性）。大小匹配的外部测试集包括129个结节（其中43个恶性结节）和126个结节（其中42个恶性结节）。算法的AUC分别为0.91（95% CI: 0.85, 0.97）和0.94（95% CI: 0.89, 0.98）。它在DL算法只处理单个CT检查的情况下表现出明显优势（AUC为0.85 [95% CI: 0.78, 0.92; P = .002]和AUC为0.89 [95% CI: 0.84, 0.95; P = .01]），以及PanCan模型（AUC为0.64 [95% CI: 0.53, 0.74; P < .001]和AUC为0.63 [95% CI: 0.52, 0.74; P < .001]）。结论使用当前和先前的低剂量CT检查的DL算法在估计肺部结节的3年恶性风险方面比仅使用单个CT检查的已建立模型更有效。临床试验登记号NCT00047385、 NCT00496977、NCT02837809 © RSNA，2023 该文章提供了补充材料。此外，请参阅本期Horst和Nishino的社论。

Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT examination (AUC, 0.85 [95% CI: 0.78, 0.92; P = .002]; and AUC, 0.89 [95% CI: 0.84, 0.95; P = .01]) and the PanCan model (AUC, 0.64 [95% CI: 0.53, 0.74; P < .001]; and AUC, 0.63 [95% CI: 0.52, 0.74; P < .001]). Conclusion A DL algorithm using current and prior low-dose CT examinations was more effective at estimating 3-year malignancy risk of pulmonary nodules than established models that only use a single CT examination. Clinical trial registration nos. NCT00047385, NCT00496977, NCT02837809 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Horst and Nishino in this issue.