Kumar Pawan, Singh Anuradha, Deshmukh Ashwin, Phulware Ravi Hari, Rastogi Sameer, Barwad Adarsh, Chandrashekhara S H, Singh Vishwajeet
1 Department of Radiodiagnosis, All India Institute of Medical Sciences , New Delhi , India.
2 Department of Pathology, All India Institute of Medical Sciences , New Delhi , India.
Br J Radiol. 2019 Feb;92(1094):20180738. doi: 10.1259/bjr.20180738. Epub 2018 Nov 7.
: To identify important qualitative and quantitative clinical and imaging features that could potentially differentiate renal primitiveneuroectodermal tumor (PNET) from various subtypes of renalcell carcinoma (RCC).
: We retrospectively reviewed 164 patients, 143 with pathologically proven RCC and 21 with pathologically proven renal PNET. Univariate analysis of each parameter was performed. In order to differentiate renal PNET from RCC subtypes and overall RCC as a group, we generated ROC curves and determined cutoff values for mean attenuation of the lesion, mass to aorta attenuation ratio and mass to renal parenchyma attenuation ratio in the nephrographic phase.
: Univariate analysis revealed 11 significant parameters for differentiating renal PNET from clear cell RCC (age, p = <0.001; size, p =< 0.001; endophytic growth pattern, p < 0.001;margin of lesion, p =< 0.001; septa within the lesion, p =< 0.001; renal vein invasion, p =< 0.001; inferior vena cava involvement, p = 0.014; enhancement of lesion less than the renal parenchyma, p = 0.008; attenuation of the lesion, p = 0.002; mass to aorta attenuation ratio, p =< 0.001; and mass to renal parenchyma attenuation ratio, p =< 0.001). Univariate analysis also revealed seven significant parameters for differentiating renal PNET from papillary RCC. For differentiating renal PNET from overall RCCs as a group, when 77.3 Hounsfield unit was used as cutoff value in nephrographic phase, the sensitivity and specificity were 71.83 and 76.92 % respectively. For differentiating renal PNET from overall RCCs as a group, when 0.57 was used as cutoff for mass to aorta enhancement ratio in nephrographic phase, the sensitivity and specificity were 80.28 and 84.62 % respectively.
: Specific qualitative and quantitative features can potentially differentiate renal PNET from various subtypes of RCC.
: The study underscores the utility of combined demographic and CT findings to potentially differentiate renal PNET from the much commoner renal neoplasm, i.e. RCC. It has management implications as if RCC is suspected, surgeons proceed with resection without need for confirmatory biopsy. On the contrary, a suspected renal PNET should proceed with biopsy followed by chemoradiotherapy, thus obviating the unnecessary morbidity and mortality.
确定可能有助于鉴别肾原始神经外胚层肿瘤(PNET)与肾细胞癌(RCC)各亚型的重要定性和定量临床及影像学特征。
我们回顾性分析了164例患者,其中143例经病理证实为RCC,21例经病理证实为肾PNET。对每个参数进行单因素分析。为了鉴别肾PNET与RCC各亚型以及作为一个整体的RCC,我们绘制了ROC曲线,并确定了肾实质期病变平均衰减、肿块与主动脉衰减比以及肿块与肾实质衰减比的临界值。
单因素分析显示,有11个显著参数可用于鉴别肾PNET与透明细胞RCC(年龄,p = <0.001;大小,p =< 0.001;内生性生长模式,p < 0.001;病变边缘,p =< 0.001;病变内间隔,p =< 0.001;肾静脉侵犯,p =< 0.001;下腔静脉受累,p = 0.014;病变强化低于肾实质,p = 0.008;病变衰减,p = 0.002;肿块与主动脉衰减比,p =< 0.001;肿块与肾实质衰减比,p =< 0.001)。单因素分析还显示,有7个显著参数可用于鉴别肾PNET与乳头状RCC。为了鉴别肾PNET与作为一个整体的RCC,当肾实质期使用77.3亨氏单位作为临界值时,敏感性和特异性分别为71.83%和76.92%。为了鉴别肾PNET与作为一个整体的RCC,当肾实质期使用0.57作为肿块与主动脉强化比的临界值时,敏感性和特异性分别为80.28%和84.62%。
特定的定性和定量特征可能有助于鉴别肾PNET与RCC的各亚型。
该研究强调了结合人口统计学和CT表现来鉴别肾PNET与更常见的肾肿瘤即RCC的实用性。这具有管理意义,因为如果怀疑是RCC,外科医生可直接进行切除而无需进行确诊性活检。相反,怀疑肾PNET时应先进行活检,然后进行放化疗,从而避免不必要的发病率和死亡率。