评估对比增强 CT (CECT) 定量测量在区分小 (≤4 cm) 透明细胞肾细胞癌 (ccRCC) 与良性肾肿瘤（包括贫脂血管平滑肌脂肪瘤 (fpAML) 和肾嗜酸细胞瘤 (RO)）方面的潜力.244 名经病理证实的 ccRCC (n = 184) 和良性肾肿瘤 (fpAML，n = 50；RO，n = 10) 患者被随机分配到训练队列 (n = 193) 和测试队列 1 (n = 51)，而外部测试队列 2（n = 50）来自另一家医院。通过测量肾质量和皮质的衰减并随后计算，从 CECT（未增强期，UP；皮质髓质期，CMP；肾造影期，NP；排泄期，EP）​​获得定量参数。进行单变量和多变量逻辑回归分析来评估这些参数与 ccRCC 之间的关联。最后，将构建的模型与放射科医生的诊断进行比较。在单变量分析中，UP 相关参数，特别是 UPC-T（皮层减去 UP 上的肿瘤衰减），显示训练队列中的 AUC 为 0.766，测试队列 1 中为 0.901，测试队列 1 中为 0.805。测试队列2。异质性相关参数SD（标准差）显示AUC分别为0.781、0.834和0.875。在多变量分析中，模型 1 结合了 UPC-T、NPC-T（皮质减去 NP 上的肿瘤衰减）、CMPT-UPT（CMP 上的肿瘤衰减减去 UP）和 SD，得出的 AUC 分别为 0.866、0.923 和 0.949。与放射科医生相比，多变量模型表现出比放射科医生的评估更高的准确性 (0.800-0.860) 和敏感性 (0.794-0.971)（准确性：0.700-0.720，敏感性：0.588-0.706）。CECT 的定量测量，特别是 UP 和异质性相关参数，有可能区分 ccRCC 和良性肾肿瘤 (fpAML、RO)。版权所有 © 2023 大学放射科医生协会。由爱思唯尔公司出版。保留所有权利。

To evaluate the potential of quantitative measurements on contrast-enhanced CT (CECT) in differentiating small (≤4 cm) clear cell renal cell carcinoma (ccRCC) from benign renal tumors, including fat-poor angiomyolipoma (fpAML) and renal oncocytoma (RO).244 patients with pathologically confirmed ccRCC (n = 184) and benign renal tumors (fpAML, n = 50; RO, n = 10) were randomly assigned into training cohort (n = 193) and test cohort 1 (n = 51), while external test cohort 2 (n = 50) was from another hospital. Quantitative parameters were obtained from CECT (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP; excretory phase, EP) by measuring attenuation of renal mass and cortex and subsequently calculated. Univariable and multivariable logistic regression analyses were performed to evaluate the association between these parameters and ccRCC. Finally, the constructed models were compared with radiologists' diagnoses.In univariable analysis, UP-related parameters, particularly UPC-T (cortex minus tumor attenuation on UP), demonstrated AUC of 0.766 in training cohort, 0.901 in test cohort 1, 0.805 in test cohort 2. The heterogeneity-related parameter SD (standard deviation) showed AUC of 0.781, 0.834, and 0.875 respectively. In multivariable analysis, model 1 incorporating UPC-T, NPC-T (cortex minus tumor attenuation on NP), CMPT-UPT (tumor attenuation on CMP minus UP), and SD yielded AUC of 0.866, 0.923, and 0.949 respectively. When compared with radiologists, multivariate models demonstrated higher accuracy (0.800-0.860) and sensitivity (0.794-0.971) than radiologists' assessments (accuracy: 0.700-0.720, sensitivity: 0.588-0.706).Quantitative measurements on CECT, particularly UP- and heterogeneity-related parameters, have potential to discriminate ccRCC and benign renal tumors (fpAML, RO).Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.