Pawan Kumar1, Anuradha Singh1, Ashwin Deshmukh1, Ravi Hari Phulware2, Sameer Rastogi3, Adarsh Barwad2, S H Chandrashekhara1, Vishwajeet Singh4. 1. 1 Department of Radiodiagnosis, All India Institute of Medical Sciences , New Delhi , India. 2. 2 Department of Pathology, All India Institute of Medical Sciences , New Delhi , India. 3. 3 Department of Medical Oncology, All India Institute of Medical Sciences , New Delhi , India. 4. 4 Department of Biostatistics, All India Institute of Medical Sciences , New Delhi , India.
Abstract
OBJECTIVE: : 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). METHODS: : 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. RESULTS: : 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. CONCLUSION: : Specific qualitative and quantitative features can potentially differentiate renal PNET from various subtypes of RCC. ADVANCES IN KNOWLEDGE:: 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.
OBJECTIVE: : 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). METHODS: : 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. RESULTS: : 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. CONCLUSION: : Specific qualitative and quantitative features can potentially differentiate renal PNET from various subtypes of RCC. ADVANCES IN KNOWLEDGE:: 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.
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