目的·探讨使用超低剂量CT（ultra-low-dose CT，ULDCT）平扫评价基于实体瘤疗效评价标准（response evaluation criteria in solid tumors，RECIST）定义的肺部靶病灶和磨玻璃结节的可行性。方法·2020年4月—6月纳入接受了胸部ULDCT平扫（0.07~0.14 mSv）和低剂量增强CT检查（2.38 mSv），而且有RECIST标准定义的可测量肺部靶病灶或直径≤1 cm磨玻璃结节的患者。每例患者均重建了4组图像，包括3组ULDCT图像，分别为80%强度的多模型自适应统计迭代重建（adaptive statistical iterative reconstruction-V with an 80% strength level，ASIR-V-80%）图像、中等强度的深度学习重建（deep learning image reconstruction of moderate strength，DLIR-M）图像和高强度的深度学习重建（deep learning image reconstruction of high strength，DLIR-H）图像，以及1组作为参考标准的增强CT图像。结果·80例患者符合入组标准，平均年龄（62±11）岁，共80个靶病灶和27个磨玻璃结节。3组ULDCT图像的肺部靶病灶测量值（r分别为0.988、0.987和0.990）、≤1 cm磨玻璃结节的直径测量值（r分别为0.905、0.906和0.969）、非肺门淋巴结靶病灶测量值（r分别为0.969、0.957和0.977）、肺门淋巴结靶病灶测量值（r分别为0.972、0.994和0.994）与增强CT有很高的相关性。Bland-Altman分析显示，DLIR-H重建图像中肺部靶病灶测量值的大小与参考值的差异为4.3%（95%一致性界限：-5.7%~14.3%），非肺门淋巴结靶病灶测量值的大小与参考值的差异为5.1%（-9.1%~19.3%），优于ASIR-V-80%［8.5%（-3.3%~20.3%），9.7%（-6.0%~25.3%）］和DLIR-M［8.5%（-4.2%~21.3%，8.8%（-9.9%~27.5%）］。DLIR-H重建图像中的肺门淋巴结病灶测量值大小与参考值的差异为18.3%（8.8%~27.9%），优于ASIR-V-80%［20.2%（-1.2%~41.5%）］和DLIR-M［23.4%（13.5%~33.2%）］。DLIR-H重建图像中磨玻璃结节测量值与参考值的差异为7.0%（-5.7%~19.7%），优于ASIR-V-80% ［14.4%（-4.4%~33.2%）］和DLIR-M［16.3%（-4.1%~36.7%）］。结论·基于DLIR-H重建的ULDCT平扫图像中的肺部靶病灶和直径≤1 cm磨玻璃结节的测量值与增强CT图像有很高的相关性和较低的差异性，有利于在大幅度降低辐射剂量下对肿瘤和磨玻璃结节进行重复扫描。

Objective ·To explore the feasibility of using noncontrast ultra-low-dose CT (ULDCT) to evaluate chest target lesions based on response evaluation criteria in solid tumors (RECIST) and ground glass nodules (GGNs). Methods ·From April to June 2020, patients who underwent noncontrast chest ULDCT (0.07?0.14 mSv) and low-dose enhanced CT (2.38 mSv) and had measurable target lesions defined by RECIST and GGNs ≤1 cm in diameter were included. Four sets of CT images were reconstructed for each patient, including 3 sets of ULDCT images, i.e., adaptive statistical iterative reconstruction-V with an 80% strength level (ASIR-V-80%), deep learning image reconstruction of moderate strength (DLIR-M) and deep learning image reconstruction of high strength (DLIR-H), and one set of enhanced CT images as the reference. Results ·Eighty patients who had 80 target lesions and 27 GGNs met the inclusion criteria, and the average age was (62±11) years old. Between the ULDCT images (3 sets of image reconstruction) and enhanced CT, the measured values of target lesions (r=0.988, 0.987 and 0.990, respectively), GGNs≤1 cm in diameter (r=0.905, 0.906 and 0.969, respectively), mediastinal lymph node target lesions (r=0.969, 0.957 and 0.977, respectively), and hilar lymph node target lesions (r=0.972, 0.994 and 0.994, respectively) were highly correlated. Bland-Altman analysis showed that the difference between the measured size of lung target lesion and reference value in DLIR-H reconstruction image was 4.3% (95% limits of agreement: -5.7%?14.3%), and the difference between the measured size of mediastinal lymph node target lesion and reference value was 5.1% (-9.1%?19.3%), which was better than that of ASIR-V-80% [8.5% (-3.3%?20.3%), 9.7% (-6.0%?25.3%)] and DLIR-M [8.5% (-4.2%?21.3%), 8.8% (-9.9%?27.5%)]. The difference between the measured size of lymph node target lesions in DLIR-H images and the reference value was 18.3% (8.8%?27.9%), better than ASIR-V-80% [20.2% (-1.2%?41.5%)] and DLIR-M [23.4% (13.5%?33.2%)]. The difference between the measured size of GGNs in DLIR-H images and the reference value was 7.0% (-5.7%?19.7%), better than ASIR-V-80% [14.4% (-4.4%?33.2%)] and DLIR-M [16.3% (-4.1%?36.7%)]. Conclusion ·Noncontrast ULDCT based on DLIR-H is highly correlated and consistent with the traditional enhanced CT in evaluating chest target lesions and GGNs (≤1 cm in diameter), which is conducive to repeated scanning of tumors and GGNs at a significantly reduced radiation dose.