Department of Radiology, Hospital Clínic de Barcelona. 170, Villarroel street, 08036 , Barcelona, Spain.
Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, NY, USA.
Br J Radiol. 2020 Nov 1;93(1115):20200064. doi: 10.1259/bjr.20200064. Epub 2020 Aug 26.
The purpose of this study is to validate a multivariable predictive model previously developed to differentiate between renal cell carcinoma (RCC) and oncocytoma using CT parameters.
We included 100 renal lesions with final diagnosis of RCC or oncocytoma studied before surgery with 4-phase multidetector CT (MDCT). We evaluated the characteristics of the tumors and the enhancement patterns at baseline, arterial, nephrographic and excretory MDCT phases.
Histopathologically 15 tumors were oncocytomas and 85 RCCs. RCCs were significantly larger (median 4.4 cm 2.8 cm, = 0.006). There were significant differences in nodule attenuation in the excretory phase compared to baseline (median: 31 42, = 0.015), with RCCs having lower values. Heterogeneous enhancement patterns were also more frequent in RCCs (85.9% 60%, = 0.027).Multivariable analysis showed that the independent predictors of malignancy were the enhancement pattern, with oncocytomas being more homogeneous in the nephrographic phase [Odds Ratio (OR) 0.16 (95% CI 0.03 to 0.75, = 0.02)], nodule enhancement in the excretory phase compared to baseline, with RCCs showing lower enhancement [OR 0.96 (95% CI 0.93 to 0.99, = 0.005)], and a size > 4 cm, with RCCs being larger [OR 5.89 (95% CI 1.10 to 31.58), = 0.038].
The multivariable predictive model previously developed which combines different MDCT parameters, including lesion size > 4 cm, lesion enhancement in the excretory phase compared to baseline and enhancement heterogeneity, can be successfully applied to distinguish RCC from oncocytoma.
This study confirms that multiparametric assessment using MDCT (including parameters such as size, homogeneity and enhancement differences between the excretory and the baseline phases) can help distinguish between RCCs and oncocytomas. While it is true that this multiparametric predictive model may not always correctly classify renal tumors such as RCC or oncocytoma, it can be used to determine which patients would benefit from pre-surgical biopsy to confirm that the tumor is in fact an oncocytoma, and thereby avoid unnecessary surgical treatments.
本研究旨在验证先前使用 CT 参数建立的用于区分肾细胞癌 (RCC) 和嗜酸细胞瘤的多变量预测模型。
我们纳入了 100 例术前经多排螺旋 CT（MDCT）检查并经最终病理诊断为 RCC 或嗜酸细胞瘤的肾脏病变。我们评估了肿瘤的特征和在基线、动脉、肾实质和排泄期 MDCT 相的增强模式。
组织病理学上，15 个肿瘤为嗜酸细胞瘤，85 个为 RCC。RCC 的肿瘤明显更大（中位数 4.4 cm×2.8 cm， = 0.006）。与基线相比，排泄期结节衰减存在显著差异（中位数：31 42， = 0.015），RCC 值较低。不均匀的增强模式在 RCC 中也更为常见（85.9% 60%， = 0.027）。多变量分析显示，恶性肿瘤的独立预测因素是增强模式，嗜酸细胞瘤在肾实质期更均匀[优势比（OR）0.16（95%可信区间 0.03 至 0.75， = 0.02）]，与基线相比，排泄期结节增强，RCC 显示较低的增强[OR 0.96（95%可信区间 0.93 至 0.99， = 0.005）]，且直径＞4 cm，RCC 较大[OR 5.89（95%可信区间 1.10 至 31.58）， = 0.038]。
先前建立的结合不同 MDCT 参数（包括直径＞4 cm、排泄期与基线相比结节增强和增强异质性）的多变量预测模型，可成功用于区分 RCC 和嗜酸细胞瘤。
本研究证实，使用 MDCT（包括大小、均匀性和排泄与基线阶段之间的增强差异等参数）的多参数评估可以帮助区分 RCC 和嗜酸细胞瘤。虽然该多参数预测模型并不总能正确分类 RCC 或嗜酸细胞瘤等肾脏肿瘤，但它可用于确定哪些患者将从术前活检中受益，以确认肿瘤实际上是嗜酸细胞瘤，从而避免不必要的手术治疗。
