LDCT年度低剂量计算机断层扫描可降低肺癌死亡率，但通过将LDCT图像与深度学习或统计模型相结合，识别低风险个体实现双年度筛查，可减少影响并提高成本效益。为了识别国家肺癌筛查试验（NLST）中的低风险个体并估计，如果它们被分配双年度筛查，有多少肺癌将被延迟1年的诊断。本次诊断性研究包括2002年1月1日至2004年12月31日间入选NLST且随访至2009年12月31日完成的预计非恶性肺结节的参与者。数据分析时间为2019年9月11日至2022年3月15日。重新校准具有外部验证的深度学习算法，该算法使用LDCT图像预测当前肺结节的恶性程度（肺癌预测卷积神经网络[LCP-CNN]; Optellum Ltd）以预测LDCT对预计非恶性结节的1年肺癌检测。根据重新校准的LCP-CNN模型、肺癌风险评估工具（LCRAT + CT [结合个体风险因素和LDCT影像特征的统计模型]）和有关肺结节的美国放射学学院建议，版本1.1（Lung-RADS），假定将预计非恶性肺结节的个体分配到年度或双年度筛查。主要结果包括模型预测性能，1年延迟癌症诊断的绝对风险，以及没有肺癌的个体分配双年度筛查间隔的比例与肺癌诊断延迟的比例。该研究包括10831张预计非恶性肺结节的LDCT图像（58.7％男性；平均[标准偏差]年龄，61.9 [5.0]岁），其中195人在随后的筛查中被诊断为患有肺癌。与LCRAT + CT（0.79）或Lung-RADS（0.69）相比，重新校准的LCP-CNN在预测1年肺癌风险方面具有更高的曲线下面积（0.87）（P＜.001）。如果66％的筛查中包含结节被分配双年度筛查，与LCRAT + CT（0.60％；P = .001）或Lung-RADS（0.97％；P＜.001）相比，重新校准的LCP-CNN的1年延迟癌症诊断的绝对风险更低（0.28％）。为延迟1年的肺癌诊断仅延迟10％，比LCRAT + CT（40.3％vs66.4％；P＜.001）更多的人可以安全地分配双年度筛查。在评估肺癌风险模型的这次诊断性研究中，重新校准的深度学习算法对于1年肺癌风险的预测最为准确，并且在被分配双年度筛查的人群中延迟1年的癌症诊断的风险最小。深度学习算法可以优先为可疑结节进行检查，并降低低风险结节的筛查强度，这可能对于在医疗保健系统中的实施至关重要。

Annual low-dose computed tomographic (LDCT) screening reduces lung cancer mortality, but harms could be reduced and cost-effectiveness improved by reusing the LDCT image in conjunction with deep learning or statistical models to identify low-risk individuals for biennial screening.To identify low-risk individuals in the National Lung Screening Trial (NLST) and estimate, had they been assigned a biennial screening, how many lung cancers would have been delayed 1 year in diagnosis.This diagnostic study included participants with a presumed nonmalignant lung nodule in the NLST between January 1, 2002, and December 31, 2004, with follow-up completed on December 31, 2009. Data were analyzed for this study from September 11, 2019, to March 15, 2022.An externally validated deep learning algorithm that predicts malignancy in current lung nodules using LDCT images (Lung Cancer Prediction Convolutional Neural Network [LCP-CNN]; Optellum Ltd) was recalibrated to predict 1-year lung cancer detection by LDCT for presumed nonmalignant nodules. Individuals with presumed nonmalignant lung nodules were hypothetically assigned annual vs biennial screening based on the recalibrated LCP-CNN model, Lung Cancer Risk Assessment Tool (LCRAT + CT [a statistical model combining individual risk factors and LDCT image features]), and the American College of Radiology recommendations for lung nodules, version 1.1 (Lung-RADS).Primary outcomes included model prediction performance, the absolute risk of a 1-year delay in cancer diagnosis, and the proportion of people without lung cancer assigned a biennial screening interval vs the proportion of cancer diagnoses delayed.The study included 10 831 LDCT images from patients with presumed nonmalignant lung nodules (58.7% men; mean [SD] age, 61.9 [5.0] years), of whom 195 were diagnosed with lung cancer from the subsequent screen. The recalibrated LCP-CNN had substantially higher area under the curve (0.87) than LCRAT + CT (0.79) or Lung-RADS (0.69) to predict 1-year lung cancer risk (P < .001). If 66% of screens with nodules were assigned to biennial screening, the absolute risk of a 1-year delay in cancer diagnosis would have been lower for recalibrated LCP-CNN (0.28%) than LCRAT + CT (0.60%; P = .001) or Lung-RADS (0.97%; P < .001). To delay only 10% of cancer diagnoses at 1 year, more people would have been safely assigned biennial screening under LCP-CNN than LCRAT + CT (66.4% vs 40.3%; P < .001).In this diagnostic study evaluating models of lung cancer risk, a recalibrated deep learning algorithm was most predictive of 1-year lung cancer risk and had least risk of 1-year delay in cancer diagnosis among people assigned biennial screening. Deep learning algorithms could prioritize people for workup of suspicious nodules and decrease screening intensity for people with low-risk nodules, which may be vital for implementation in health care systems.