Overlapping Genetic Background of Coronary Artery and Carotid/Femoral Atherosclerotic Calcification.

BACKGROUND AND OBJECTIVES: Multivessel atherosclerosis and its genetic background are under-investigated, although atherosclerosis is seldom local and still causes high mortality. Alternative methods to assess coronary calcification (CAC) might incorporate genetic links between different arteries' atherosclerotic involvement, however, co-occurrences of coronary calcification have not been investigated in twins yet. MATERIALS AND METHODS: We assessed the heritability of radio morphologically distinct atherosclerotic plaque types in coronary (non-enhanced CT, Agatston score), carotid, and femoral arteries (B-mode ultrasound) in 190 twin subjects (60 monozygotic, 35 dizygotic pairs). Four-segment scores were derived in order to assess the dissemination of the distinct plaque types in the carotid and femoral arteries taking bilaterality into account. We calculated the genetic correlation between phenotypically correlating plaque types in these arteries. RESULTS: CAC and dissemination of calcified plaques in the carotid and femoral arteries (4S_hyper) were moderately heritable (0.67 [95% CI: 0.37-1] and 0.69 [95% CI: 0.38-1], respectively) when adjusted for age and sex. Hypoechoic plaques in the carotid and femoral arteries showed no heritability, while mixed plaques showed intermediate heritability (0.50 [95% CI: 0-0.76]). Age and sex-adjusted phenotypic correlation between CAC and 4segm_hyper was 0.48 [95% CI: 0.30-0.63] and the underlying genetic correlation was 0.86 [95% CI: 0.42-1]. CONCLUSIONS: Calcification of atherosclerotic plaques is moderately heritable in all investigated arteries and significant overlapping genetic factors can be attributed to the phenotypical resemblance of coronary and carotid or femoral atherosclerotic calcification. Our findings support the idea of screening extracoronary arteries in asymptomatic individuals. We also propose a hypothesis about primarily carotid-coronary and femoral-coronary atherosclerosis as two distinct genetic predispositions to co-localization.

Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis.

BACKGROUND: CT texture analysis (CTTA) has been successfully used to assess tissue heterogeneity in multiple diseases. The purpose of this work is to demonstrate the value of three-dimensional CTTA in the evaluation of diffuse liver disease. We aimed to develop CTTA based prediction models, which can be used for staging of fibrosis in different anatomic liver segments irrespective of variations in scanning parameters. METHODS: We retrospectively collected CT scans of thirty-two chronic hepatitis patients with liver fibrosis. The CT examinations were performed on either a 16- or a 64-slice scanner. Altogether 354 anatomic liver segments were manually highlighted on portal venous phase images, and 1117 three-dimensional texture parameters were calculated from each segment. The segments were divided between groups of low-grade and high-grade fibrosis using shear-wave elastography. The highly-correlated features (Pearson r > 0.95) were filtered out, and the remaining 453 features were normalized and used in a classification with k-means and hierarchical cluster analysis. The segments were split between the train and test sets in equal proportion (analysis I) or based on the scanner type (analysis II) into 64-slice train 16-slice validation cohorts for machine learning classification, and a subset of highly prognostic features was selected with recursive feature elimination. RESULTS: A classification with k-means and hierarchical cluster analysis divided segments into four main clusters. The average CT density was significantly higher in cluster-4 (110 HU ± SD = 10.1HU) compared to the other clusters (c1: 96.1 HU ± SD = 11.3HU; p < 0.0001; c2: 90.8 HU ± SD = 16.8HU; p < 0.0001; c3: 93.1 HU ± SD = 17.5HU; p < 0.0001); but there was no difference in liver stiffness or scanner type among the clusters. The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85-0.95 vs. AUC = 0.88, CI = 0.84-0.91). The final support vector machine model achieved an excellent prediction rate in the second analysis (AUC = 0.91, CI = 0.88-0.94) and an acceptable prediction rate in the first analysis (AUC = 0.76, CI = 0.67-0.84). CONCLUSIONS: In conclusion, CTTA-based models can be successfully applied to differentiate high-grade from low-grade fibrosis irrespective of the imaging platform. Thus, CTTA may be useful in the non-invasive prognostication of patients with chronic liver disease.

Interobserver agreement and diagnostic accuracy of shearwave elastography for the staging of hepatitis C virus-associated liver fibrosis.

PURPOSE: Our study aimed to evaluate the technical success rate, interobserver reproducibility, and accuracy of shearwave elastography (SWE) in the staging of hepatitis C virus (HCV)-associated liver fibrosis. METHODS: A total of 10 healthy controls and 49 patients with chronic liver disease were enrolled prospectively. Two examiners performed point shearwave elastography (pSWE) and two-dimensional shearwave elastography (2D-SWE) measurements with an RS85A ultrasound scanner using the S-Shearwave application (Samsung Medison, Hongcheon, Korea). The performance of S-Shearwave in the staging (METAVIR F0-F4) of liver fibrosis was compared with prior transient elastography (TE) with receiver operating characteristic (ROC) curve analysis. RESULTS: The interobserver reproducibility was excellent with pSWE (ICC = 0.92, 95% CI: 0.86-0.95, P < .001). A very good agreement was found between pSWE and TE measurements (ICC =0.85, 95% CI: 0.78-0.89, P < .001). The ROC analysis determined the optimal cut-off values of pSWE for the staging of chronic hepatitis C-associated fibrosis (F2, 1.46 m/s; F3, 1.63 m/s; F4, 1.95 m/s). Both observers achieved excellent diagnostic accuracy (AUROC: 94% vs 97%) in the detection of significant (≥F2) liver fibrosis. CONCLUSION: The interobserver agreement is excellent with S-Shearwave pSWE, and observers can diagnose significant liver fibrosis with a comparable accuracy to TE.

Transitional cell and clear cell renal carcinoma: differentiation of distinct histological types with multiphase CT.

BACKGROUND: Transitional cell carcinoma (TCC) may mimic renal cell carcinoma (RCC) when it develops in a similar location, therefore, differentiation with imaging techniques might be challenging. Preoperative differentiation may have a significant role indicating the type of surgical treatment (nephrectomy vs. ureteronephrectomy). PURPOSE: To retrospectively analyze the differences in the contrast enhancement of TCC and RCC. MATERIAL AND METHODS: Images of 20 RCC and 12 TCC (mean ages, 62.3 ± 14.1 and 67.4 ± 12.0 years, respectively) were analyzed from patients who underwent multiphase computed tomography (CT) examinations following 1.5 mL/kg non-ionic contrast agent administration. Unenhanced corticomedullary (30-45 s), nephrographic (70-90 s), and excretory (300-480 s) phases were imaged. The attenuation characteristics of RCC and TCC were compared to the attenuation of the normal renal cortex. RESULTS: Significant differences were found in the attenuation ratios between RCC or TCC in the corticomedullary (P = 0.040) and nephrographic (P = 0.004) phases using three regions of interest (ROIs) of 10 mm(2) size. If measuring ROIs comprising the complete tumor lesion instead of three small ROIs, no significant difference was observed in the attenuation ratios between RCC in TCC in any phases. CONCLUSION: Our study reports significant attenuation differences between RCC and TCC in the corticomedullary and nephrographic phases by multiphase CT. The findings underscore the importance of multiphase CT in the differentiation of these two different entities. Using multiple small (three) ROIs is more accurate than measuring the whole tumor attenuation.

Bosniak category III cysts are more likely to be malignant than we expected in the era of multidetector computed tomography technology.

BACKGROUND: Complex indeterminate Bosniak category III renal cystic masses are traditionally considered to be malignant in 50%. Our aim was to retrospectively evaluate the attenuation characteristics in multiphase computed tomography (CT) and to determinate the incidence of malignancy based on histological findings on all Bosniak category III renal cystic masses investigated in our department between April 3, 2007 and November 21, 2013. MATERIALS AND METHODS: QUADRIPHASIC MULTIDETECTOR CT IMAGES OF NINETEEN PATIENTS (MEAN AGE: 56.5 ± 16.5 years) with radiologically detected Bosniak category III lesions were reviewed retrospectively. All lesions were surgically removed, and the incidence of malignancy, based on pathological results was determined. RESULTS: Calcification was present in four lesions (21%). The mean largest diameter was 48.7 ± 28.8 mm. All lesions were multilobulated and septated. Of the 19 removed lesions, 16 (84%) were malignant, and 3 (16%) were benign (one inflammatory cyst including a nephrolith, one cystic nephroma and one atypical angiomyolipoma). CT and histological findings of 19 Bosniak III cysts were correlated. CONCLUSION: Our study demonstrated much higher prevalence of malignancy (84%) in radiologically detected Bosniak category III cysts than it has been described before. It may due to the era of modern multidetector CT technology and multiphase protocol.

The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia.

OBJECTIVE: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test. METHODS: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort. RESULTS: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or "COVID-19 without virus detection", as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%. CONCLUSION: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making. ADVANCES IN KNOWLEDGE: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.

Radiomics analysis of contrast-enhanced CT scans can distinguish between clear cell and non-clear cell renal cell carcinoma in different imaging protocols.

Introduction: This study aimed to construct a radiomics-based machine learning (ML) model for differentiation between non-clear cell and clear cell renal cell carcinomas (ccRCC) that is robust against institutional imaging protocols and scanners. Materials and methods: Preoperative unenhanced (UN), corticomedullary (CM), and excretory (EX) phase CT scans from 209 patients diagnosed with RCCs were retrospectively collected. After the three-dimensional segmentation, 107 radiomics features (RFs) were extracted from the tumor volumes in each contrast phase. For the ML analysis, the cases were randomly split into training and test sets with a 3:1 ratio. Highly correlated RFs were filtered out based on Pearson's correlation coefficient (r > 0.95). Intraclass correlation coefficient analysis was used to select RFs with excellent reproducibility (ICC ≥ 0.90). The most predictive RFs were selected by the least absolute shrinkage and selection operator (LASSO). A support vector machine algorithm-based binary classifier (SVC) was constructed to predict tumor types and its performance was evaluated based-on receiver operating characteristic curve (ROC) analysis. The "Kidney Tumor Segmentation 2019" (KiTS19) publicly available dataset was used during external validation of the model. The performance of the SVC was also compared with an expert radiologist's. Results: The training set consisted of 121 ccRCCs and 38 non-ccRCCs, while the independent internal test set contained 40 ccRCCs and 13 non-ccRCCs. For external validation, 50 ccRCCs and 23 non-ccRCCs were identified from the KiTS19 dataset with the available UN, CM, and EX phase CTs. After filtering out the highly correlated and poorly reproducible features, the LASSO algorithm selected 10 CM phase RFs that were then used for model construction. During external validation, the SVC achieved an area under the ROC curve (AUC) value, accuracy, sensitivity, and specificity of 0.83, 0.78, 0.80, and 0.74, respectively. UN and/or EX phase RFs did not further increase the model's performance. Meanwhile, in the same comparison, the expert radiologist achieved similar performance with an AUC of 0.77, an accuracy of 0.79, a sensitivity of 0.84, and a specificity of 0.69. Conclusion: Radiomics analysis of CM phase CT scans combined with ML can achieve comparable performance with an expert radiologist in differentiating ccRCCs from non-ccRCCs.

The Predictive Role of Artificial Intelligence-Based Chest CT Quantification in Patients with COVID-19 Pneumonia.

We sought to analyze the prognostic value of laboratory and clinical data, and an artificial intelligence (AI)-based algorithm for Coronavirus disease 2019 (COVID-19) severity scoring, on CT-scans of patients hospitalized with COVID-19. Moreover, we aimed to determine personalized probabilities of clinical deterioration. Data of symptomatic patients with COVID-19 who underwent chest-CT-examination at the time of hospital admission between April and November 2020 were analyzed. COVID-19 severity score was automatically quantified for each pulmonary lobe as the percentage of affected lung parenchyma with the AI-based algorithm. Clinical deterioration was defined as a composite of admission to the intensive care unit, need for invasive mechanical ventilation, use of vasopressors or in-hospital mortality. In total 326 consecutive patients were included in the analysis (mean age 66.7 ± 15.3 years, 52.1% male) of whom 85 (26.1%) experienced clinical deterioration. In the multivariable regression analysis prior myocardial infarction (OR = 2.81, 95% CI = 1.12-7.04, p = 0.027), immunodeficiency (OR = 2.08, 95% CI = 1.02-4.25, p = 0.043), C-reactive protein (OR = 1.73, 95% CI = 1.32-2.33, p < 0.001) and AI-based COVID-19 severity score (OR = 1.08; 95% CI = 1.02-1.15, p = 0.013) appeared to be independent predictors of clinical deterioration. Personalized probability values were determined. AI-based COVID-19 severity score assessed at hospital admission can provide additional information about the prognosis of COVID-19, possibly serving as a useful tool for individualized risk-stratification.

Genetic and environmental factors on heart rate, mean arterial pressure and carotid intima-media thickness: A longitudinal twin study.

BACKGROUND: Heart rate (HR), mean arterial pressure (MAP) and carotid intima-media thickness (cIMT) are moderately heritable cardiovascular traits, but the environmental effects on the longitudinal change of their heritability have never been investigated. METHODS: 368 Italian and Hungarian twins (107 monozygotic, 77 dizygotic) underwent oscillometric measurement and B-mode sonography of bilateral carotid arteries in 2009/2010 and 2014. Within- -individual/cross-study wave, cross-twin/within-study wave and cross-twin/cross-study wave correlations were estimated, and bivariate Cholesky models were fitted to decompose the total variance at each wave and covariance between study waves into additive genetic, shared and unique environmental components. RESULTS: For each trait, a moderate longitudinal stability was observed, with within-individual/crosswave correlations of 0.42 (95% CI: 0.33-0.51) for HR, 0.34 (95% CI: 0.24-0.43) for MAP, and 0.23 (95% CI: 0.12-0.33) for cIMT. Cross-twin/cross-wave correlations in monozygotic pairs were all significant and substantially higher than the corresponding dizygotic correlations. Genetic continuity was the main source of longitudinal stability, with across-time genetic correlations of 0.52 (95% CI: 0.29-0.71) for HR, 0.56 (95% CI: 0.31-0.81) for MAP, and 0.36 (95% CI: 0.07-0.64) for cIMT. Overlapping genetic factors explained respectively 57%, 77%, and 68% of the longitudinal covariance of the HR, MAP and cIMT traits. CONCLUSIONS: Genetic factors have a substantial role in the longitudinal change of HR, MAP and cIMT; however, the influence of unique environmental factors remains relevant. Further studies should better elucidate whether epigenetic mechanisms have a role in influencing the stability of the investigated traits over time.

Our initial experiences with mpMRI-ultrasound fusion-guided prostate biopsy / Kezdeti tapasztalataink az mpMR fúziós ultrahangvezérelt prosztatabiopsziával

Introduction: The past decade has seen some major changes in the diagnostics of prostate cancer. Progress in MR imaging has allowed us to better visualise prostate cancer and thus perform targeted biopsies of tumour suspect lesions. mpMRI-ultrasound fusion-guided prostate biopsy is a precise and cost-effective method to diagnose prostate cancer. Objective: The purpose of this study was to summarise our results in mpMRI-ultrasound fusion biopsy between 2017 and 2019 and compare them with the findings in the current literature. Method: Between 2017 and 2019, fully 40, mpMRI-ultrasound fusion biopsies were performed transperineally using the BioJet fusion system at Semmelweis University Urology Clinic. The MRI evaluations were done in line with the PI-RADS v2 guidelines. It was analysed whether the PI-RADS score, the location of the tumour, lesion size, the signs of extraprostatic extension, PSA/PSAD density and prostate volume have an influence on the outcome of mpMRI-ultrasound fusion biopsy. Results: Prostate cancer was diagnosed in 80% of the cases during targeted biopsies. The detection rate was 91%, 85%, and 20% for PI-RADS 5, 4 and 3 lesions, respectively. The detection rate was significantly higher for lesions located at the peripheral zone compared to the ones in the transitional zone (khi2(1) = 6.555, p = 0.010, Fisher-exact p = 0.017, V = 0.355). Signs of extraprostatic extension and higher PSAD correlated with better detection rate (khi2(1)= 7.704, p = 0.006, Fisher-exact p = 0.004, V = 0.355; and 0.47 ± 0.50 ng/ml2 vs. 0.18 ± 0.17 ng/ml2; Z = 3.447, p<0.001, respectively). The size of the lesions did not influence the outcome. The analysis showed a significant correlation between large prostate volumes and negative biopsies (50.9 ± 18.8 ml vs. 119.6 ± 91.6 ml; Z= ­3.505, p<0.001). Conclusions: The detection rate of prostate cancer with targeted biopsies was higher than the data found in the international literature. The PI-RADS score, the location of the tumour, MRI signs of extraprostatic extension, PSAD and prostate volume had an influence on the detection rate. Our findings may promote a better selection of the best candidates for targeted biopsies in the future.

Genetic and environmental determinants of longitudinal stability of arterial stiffness and wave reflection: a twin study.

BACKGROUND: We aimed at evaluating the impact of genetic and environmental factors on longitudinal changes in aortic pulse wave velocity (aPWV) and aortic augmentation index (aAIx). METHOD: Three hundred and sixty-eight Italian and Hungarian adult twins (214 monozygotic, 154 dizygotic) underwent repeated evaluations of aPWV and aAIx (TensioMed Arteriograph). Within-individual/cross-wave, cross-twin/within-wave and cross-twin/cross-wave correlations were calculated; bivariate Cholesky models were fitted to calculate additive genetic (A), shared environmental (C) and unique environmental (E) components. RESULTS: For both aPWV and aAIx, cross-twin correlations in monozygotic pairs (r between 0.35 and 0.56) were all significant and always higher than in dizygotic pairs, both at wave 1 and at wave 2. Heritability and unshared environmental proportion of variance at each wave were substantially time-invariant for aPWV (heritability 0.51, 95% CI 0.36-0.63 at wave 1; 0.49, 95% CI 0.34-0.62 at wave 2), whereas for aAIx, we observed a diminished genetic effect (heritability 0.57, 95% CI 0.45-0.67 at wave 1; 0.37, 95% CI 0.21-0.51 at wave 2). Overlapping genetic factors explained a high proportion (0.88, 95% CI 0.61-1.00) of longitudinal covariance for aPWV, and had a relatively lower impact on aAIx (0.55, 95% CI 0.35-0.70). Genetic correlations of aPWV (r = 0.64, 95% CI 0.42-0.85) and aAIx (r = 0.70, 95% CI 0.52-0.87) between waves were lower than 1, suggesting a potential contribution of novel genetic variance on arterial stiffening. CONCLUSION: Changes in aPWV and aAIx over time are largely genetically determined. Our results might stimulate further studies on genetic and epigenetic factors influencing the process of vascular ageing.

Genetic influence on femoral plaque and its relationship with carotid plaque: an international twin study.

To disentangle genetic and environmental influences on the development of femoral plaques using a population of adult twins. To evaluate the potential role of shared genetic and environmental factors in the co-occurrence of femoral and carotid plaques. The sample included 566 twins belonging to 164 monozygotic (MZ) and 119 dizygotic (DZ) twin pairs, who underwent peripheral arterial assessment by B-mode ultrasound in different centers. The variance in femoral plaques onset was due to genetic factors and the remaining 50% was explained by common (15%) and unique (35%) environmental factors. Findings on sidedness and number of femoral plaques indicated that also these traits were mainly under genetic control. No effect of common environment was found on plaques composition, and variability of this trait was explained by genetics (64%) and unique environment (36%). Covariation between the liabilities to carotid and femoral plaques was mainly attributed to shared genes (77%), with the remaining 23% explained by individual-specific environmental factors shared by the two districts. Inter-individual differences in plaque onset as well as in their number, sidedness and composition are mainly genetic in origin. The results on the cooccurrence of carotid and femoral plaque underline the genetic role in atherogenesis.

Vertebral Artery Diameter and Flow: Nature or Nurture.

BACKGROUND AND PURPOSE: In contrast with the carotid arteries, the vertebral arteries (VAs) show considerable variation in length, caliber, and vessel course. This study investigated whether the variation in diameter and flow characteristics of the VAs might be inherited. METHODS: A total of 172 Italian twins from Padua, Perugia, and Terni (54 monozygotic, 32 dizygotic) recruited from the Italian Twin Registry underwent B-mode and pulsed-wave Doppler ultrasound assessment of their VAs. VA diameters, peak systolic velocity (PSV) and end diastolic velocity (EDV) were assessed at the level of a horizontal V2 segment. Univariate quantitative genetic modeling was performed. RESULTS: Fourteen percent of the sample had VA hypoplasia. Within pair correlation in monozygotic twins was higher than in dizygotics (.552 vs. .229) for VA diameter. Age- and sex-adjusted genetic effect, under the most parsimonious model, accounted for 54.7% (95% CI: 42.2-69.1%) of the variance of VA diameter, and unshared environmental effect for 45.3% (95% CI: 30.9-57.8%). No heritability was found for the PSV of VA, but shared (34.1%; 95% CI: 16.7-53.7%) and unshared (65.9%; 95% CI: 45.9-83.1%) environmental factors determined the variance. EDV of VA is moderately genetically influenced (42.4%; 95% CI: 16.1-64.9%) and also determined by the unshared environment (57.6%; 95% CI: 34.7-83.7%). CONCLUSIONS: The diameter of the VAs is moderately genetically determined. Different factors influence the PSV and EDV of VAs, which may highlight the complex hemodynamic background of VA flow and help to understand the vertebral flow anomalies found by ultrasound.

Heritability of the femoral intima media thickness.

BACKGROUND: The measurement of femoral intima-media thickness (IMT) is underutilized in the clinical practice, although it is a surrogate marker of cardiovascular disease. MATERIALS AND METHODS: 388 Hungarian and Italian twins (121 monozygotic, 73 dizygotic pairs) underwent bilateral B-mode sonography of femoral arteries. IMT was measured by semiautomated software, where available, or by calipers. RESULTS: Within-pair correlation in monozygotic twins was higher than in dizygotics for each parameter. Age-, sex- and country-adjusted genetic effect accounted for 43.9% (95% confidence interval, CI 21.3%-65.2%) and 47.2% (95% CI, 31.4%-62.6%) of the variance of common and superficial femoral artery IMT, respectively, and unshared environmental effect for 56.1% (95% CI 34.6%-78.5%) and 52.8% (95% CI, 37.2%-68.5%). These results did not change significantly after correcting for body mass index or central systolic blood pressure. CONCLUSIONS: Genetic factors have a moderate role in the determination of common and superficial femoral IMT; however, the influence of environmental (lifestyle) factors remains still relevant. Environmental factors may have a role in influencing the genetic predisposition for femoral vascular hypertrophy.

Clear cell renal cell carcinoma and papillary renal cell carcinoma: differentiation of distinct histological types with multiphase CT.

PURPOSE: Conventional clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) have different behavioral characteristics and clinical management strategies (nephrectomy vs. nephron-sparing surgery). Our aim was to retrospectively evaluate the contrast enhancement pattern of ccRCC and pRCC and evaluate its possible diagnostic role for preoperative differentiation using a standardized protocol. MATERIALS AND METHODS: Quadriphasic multidetector computed tomography (CT) images (unenhanced, corticomedullary, nephrographic, and excretory phases) of 19 patients with 20 ccRCC and 14 patients with 15 pRCC lesions (mean ages, 62.3±14.1 and 61.4±13.7 years, respectively) were reviewed retrospectively. The attenuation characteristics were compared with the attenuation of the normal renal cortex using either multiple 10 mm2 regions of interest or whole tumor attenuation measurements. The degree of contrast enhancement was also compared. RESULTS: Univariate analysis revealed that ccRCC lesions showed higher mean attenuation values on the corticomedullary and nephrographic phases compared with pRCC masses (P < 0.05) using both measurement techniques. CONCLUSION: The findings underscore the importance of multiphase CT in the differentiation of these two subtypes of RCC using standard assessment techniques. The measurement of the degree of enhancement on contrast-enhanced multidetector CT may be a simple and useful method to radiologically differentiate between the two histological types of RCC.