BACKGROUND The aim of the study was to derive and compare metabolic parameters relating to benign and malignant pulmonary nodules using dynamic 2-deoxy-2-[fluorine-18]fluoro-D-glucose (18F-FDG) PET/CT, and nodule perfusion parameters derived through perfusion computed tomography (CT). PATIENTS AND METHODS Twenty patients with 21 pulmonary nodules incidentally detected on CT underwent a dynamic 18F-FDG PET/CT and a perfusion CT. The maximum standardized uptake value (SUVmax) was measured on conventional 18F-FDG PET/CT images. The influx constant (Ki ) was calculated from the dynamic 18F-FDG PET/CT data using Patlak model. Arterial flow (AF) using the maximum slope model and blood volume (BV) using the Patlak plot method for each nodule were calculated from the perfusion CT data. All nodules were characterized as malignant or benign based on histopathology or 2 year follow up CT. All parameters were statistically compared between the two groups using the nonparametric Mann-Whitney test. RESULTS Twelve malignant and 9 benign lung nodules were analysed (median size 20.1 mm, 9-29 mm) in 21 patients (male/female = 11/9; mean age ± SD: 65.3 ± 7.4; age range: 50-76 years). The average SUVmax values ± SD of the benign and malignant nodules were 2.2 ± 1.7 vs. 7.0 ± 4.5, respectively (p = 0.0148). Average Ki values in benign and malignant nodules were 0.0057 ± 0.0071 and 0.0230 ± 0.0155 min-1, respectively (p = 0.0311). Average BV for the benign and malignant nodules were 11.6857 ± 6.7347 and 28.3400 ± 15.9672 ml/100 ml, respectively (p = 0.0250). Average AF for the benign and malignant nodules were 74.4571 ± 89.0321 and 89.200 ± 49.8883 ml/100g/min, respectively (p = 0.1613). CONCLUSIONS Dynamic 18F-FDG PET/CT and perfusion CT derived blood volume had similar capability to differentiate benign from malignant lung nodules.

中文翻译：

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使用 18F-FDG PET/CT 和 CT 灌注的动态成像能否区分良性和恶性肺结节？

背景 该研究的目的是使用动态 2-脱氧-2-[氟-18]氟-D-葡萄糖 (18F-FDG) PET/CT 和结节灌注推导和比较与良性和恶性肺结节相关的代谢参数通过灌注计算机断层扫描 (CT) 得出的参数。患者和方法 20 名患者在 CT 上偶然检测到 21 个肺部结节，接受了动态 18F-FDG PET/CT 和灌注 CT。在常规 18F-FDG PET/CT 图像上测量最大标准化摄取值 (SUVmax)。使用 Patlak 模型从动态 18 F-FDG PET/CT 数据计算流入常数 (Ki)。动脉血流 (AF) 使用最大斜率模型和血容量 (BV) 使用 Patlak 绘图方法计算每个结节的灌注 CT 数据。根据组织病理学或 2 年随访 CT，所有结节的特征为恶性或良性。使用非参数 Mann-Whitney 检验对两组之间的所有参数进行统计比较。结果 分析了 21 名患者（男性/女性 = 11/9；平均年龄 ± SD：65.3 ± 7.4；年龄范围：50-76 岁）的 12 个恶性肺结节和 9 个良性肺结节（中位大小 20.1 毫米，9-29 毫米） . 良性和恶性结节的平均 SUVmax 值 ± SD 分别为 2.2 ± 1.7 和 7.0 ± 4.5 (p = 0.0148)。良性和恶性结节的平均 Ki 值分别为 0.0057 ± 0.0071 和 0.0230 ± 0.0155 min-1 (p = 0.0311)。良性和恶性结节的平均 BV 分别为 11.6857 ± 6.7347 和 28.3400 ± 15.9672 ml/100 ml (p = 0.0250)。良性和恶性结节的平均 AF 分别为 74.4571 ± 89.0321 和 89。分别为 200 ± 49.8883 毫升/100 克/分钟 (p = 0.1613)。结论 动态 18F-FDG PET/CT 和灌注 CT 衍生血容量具有相似的区分肺结节良恶性的能力。