Corticomedullary Phase Research Articles

Automated segmentation of kidney and renal mass and automated detection of renal mass in CT urography using 3D U-Net-based deep convolutional neural network.

To develop a 3D U-Net-based deep learning model for automated segmentation of kidney and renal mass, and detection of renal mass in corticomedullary phase of computed tomography urography (CTU). Data on 882 kidneys obtained from CTU data of 441 patients with renal mass were used to learn and evaluate the deep learning model. The CTU data of 35 patients with small renal tumors (diameter ≤ 1.5 cm) were used for additional testing. The ground truth data for the kidney, renal tumor, and cyst were manually annotated on corticomedullary phase images of CTU. The proposed segmentation model for kidney and renal mass was constructed based on a 3D U-Net. The segmentation accuracy was evaluated through the Dice similarity coefficient (DSC). The volume of the maximum 3D volume of interest of renal tumor and cyst in the predicted segmentation by the model was used as an identification indicator, while the detection performance of the model was evaluated by the area under the receiver operation characteristic curve. The proposed model showed a high accuracy in segmentation of kidney and renal tumor, with average DSC of 0.973 and 0.844, respectively. It performed moderately in the renal cyst segmentation, with an average DSC of 0.536 in the test set. Also, this model showed good performance in detecting renal tumor and cyst. The proposed automated segmentation and detection model based on 3D U-Net shows promising results for the segmentation of kidney and renal tumor, and the detection of renal tumor and cyst. • The segmentation model based on 3D U-Net showed high accuracy in segmentation of kidney and renal neoplasm, and good detection performance of renal neoplasm and cyst in corticomedullary phase of CTU. • The segmentation model based on 3D U-Net is a fully automated aided diagnostic tool that could be used to reduce the workload of radiologists and improve the accuracy of diagnosis. • The segmentation model based on 3D U-Net would be helpful to provide quantitative information for diagnosis, treatment, surgical planning, etc.

Comparison of cortico-medullary phase contrast-enhanced MDCT and T2-weighted MR imaging in the histological subtype differentiation of renal cell carcinoma: radiology-pathology correlation.

PurposeRenal cell carcinoma (RCC) subtype differentiation is of crucial importance in the management and prognosis of these patients. In this study, we investigated the usefulness of unenhanced and cortico-medullary phase contrast-enhanced multidetector-row computed tomography (MDCT) and T2-weighted fast spin-echo (FSE) magnetic resonance imaging (MRI) modalities in the discrimination of the 3 main subtype RCC patients in correlation with their histopathological findings.Material and methodsA total of 80 pathologically proven RCC patients who had undergone either partial or total nephrectomy were retrospectively investigated in this study. Their histological subtypes were 54 clear cell renal cell carcinoma (ccRCC), 15 papillary renal cell carcinoma (pRCC), and 11 chromophobe renal cell carcinoma (cRCC), based on pathological evaluation. There were 62 male (77.5%) and 18 female (22.5%) patients. Among the 54 ccRCC patients, 29 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 5 only had cortico-medullary phase CT, and 38 had MRI examination. In the pRCC group, 10 patients had both non-contrast and cortico-medullary phase CT, 1 had only non-contrast CT, 1 had only cortico-medullary phase CT, and 12 had MRI. Finally, in the remaining 11 cRCC patients, 9 had both non-contrast and cortico-medullary phase CT, and only 5 had MRI. We calculated both tumour attenuation values as HU (Hounsfield units) on unenhanced and cortico-medullary phase MDCT images and also tumour mean signal intensity values on FSE T2-weighted MRI images by using the region of interest (ROI) including normal renal cortex measurements. Besides quantitative evaluation, we also performed qualitative visual assessment of tumours on contrast-enhanced MDCT and FSE T2-weighted MRI.ResultsThere was no statistically significant difference among the attenuation values of the 3 tumour subtypes on pre-contrast CT images. ccRCC demonstrated a prominent degree of contrast enhancement compared to the chromophobe and papillary ones on cortico-medullary phase MDCT. We found no statistically significant difference between chromophobe and papillary subtypes, although chromophobe tumours showed slightly higher attenuation values compared to papillary ones. ccRCCs usually demonstrated a heterogenous contrast enhancement on cortico-medullary phase CT images, while the papillary subtype usually had a homogenous appearance on visual assessment. On FSE T2-weighted MR images, the signal intensity values of ccRCC patients were found to be significantly higher than both chromophobe and papillary subtypes. Although cRCC patients had a prominently lower T2 signal intensity than clear cell subtype, there was no statistically significant signal intensity difference between chromophobe and papillary subtypes. Regarding visual assessment, papillary subtype tumours showed a mostly homogenous appearance on T2-weighted images and a statistically significant difference was present. On the other hand, there was no significant difference of visual assessment of the clear cell and chromophobe subtypes.ConclusionsThe measurement of the attenuation values on cortico-medullary phase MDCT and the mean signal intensity values on FSE T2-weighted MRI can provide useful information in the differentiation of RCC main subtypes. Also, visual assessment of tumours on both modalities can contribute to this issue by providing additional imaging properties.

Multidetector CT Characteristics of Fumarate Hydratase-Deficient Renal Cell Carcinoma and Papillary Type II Renal Cell Carcinoma.

ObjectiveTo investigate the multidetector computed tomography (MDCT) features of fumarate hydratase-deficient renal cell carcinoma (FH-deficient RCC) with germline or somatic mutations, and compare them with those of papillary type II RCC (pRCC type II).Materials and MethodsA total of 24 patients (mean ± standard deviation, 40.4 ± 14.7 years) with pathologically confirmed FH-deficient RCC (15 with germline and 9 with somatic mutations) and 54 patients (58.6 ± 12.6 years) with pRCC type II were enrolled. The MDCT features were retrospectively reviewed and compared between the two entities and mutation subgroups, and were correlated with the clinicopathological findings.ResultsAll the lesions were unilateral and single. Compared with pRCC type II, FH-deficient RCC was more prevalent among younger patients (40.4 ± 14.7 vs. 58.6 ± 12.6, p < 0.001) and tended to be larger (8.1 ± 4.1 vs. 5.4 ± 3.2, p = 0.002). Cystic solid patterns were more common in FH-deficient RCC (20/24 vs. 16/54, p < 0.001), with 16 of the 20 (80.0%) cystic solid tumors having showed typical polycystic and thin smooth walls and/or septa, with an eccentric solid component. Lymph node (16/24 vs. 16/54, p = 0.003) and distant (11/24 vs. 3/54, p < 0.001) metastases were more frequent in FH-deficient RCC. FH-deficient RCC and pRCC type II showed similar attenuation in the unenhanced phase. The attenuation in the corticomedullary phase (CMP) (76.3% ± 25.0% vs. 60.2 ± 23.6, p = 0.008) and nephrographic phase (NP) (87.7 ± 20.5, vs. 71.2 ± 23.9, p = 0.004), absolute enhancement in CMP (39.0 ± 24.8 vs. 27.1 ± 22.7, p = 0.001) and NP (50.5 ± 20.5 vs. 38.2 ± 21.9, p = 0.001), and relative enhancement ratio to the renal cortex in CMP (0.35 ± 0.26 vs. 0.24 ± 0.19, p = 0.001) and NP (0.43 ± 0.24 vs. 0.29 ± 0.19, p < 0.001) were significantly higher in FH-deficient RCC. No significant difference was found between the FH germline and somatic mutation subgroups in any of the parameters.ConclusionThe MDCT features of FH-deficient RCC were different from those of pRCC type II, whereas there was no statistical difference between the germline and somatic mutation subgroups. A kidney mass with a cystic solid pattern and metastatic tendency, especially in young patients, should be considered for FH-deficient RCC.

Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features.

Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92–1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.

Imaging manifestations of congenital mesoblastic nephroma

ObjectiveCongenital mesoblastic nephroma (CMN) is a rare renal tumor mainly observed in infants and young children. This study aims to analyze the imaging manifestations of CMN to improve the understanding of the disease. MethodsThe imaging manifestations and clinical records of all pediatric patients with CMN admitted to our hospital over the last 7 years were retrospectively analyzed. The diagnosis of CMN was confirmed by postoperative pathology. All patients underwent computed tomography (CT) scans; 2 patients additionally underwent magnetic resonance imaging (MRI) scans (including one prenatal MRI scan). ResultsWe evaluated 10 pediatric patients (6 males and 4 females) aged 7 days to 12 months (median age: 4 months) with CMN located on the left kidney in six cases and the right kidney in four cases. The CT imaging manifested as solid lesions (5 cases), solid-cystic lesions with solid predominance (4 cases), or solid-multicystic lesions with cystic predominance (1 case). Enhanced CT showed moderately and heterogeneously enhanced solid component and intracystic septations at the corticomedullary phase that were further enhanced at the nephrographic phase, although their CT values were still lower than those of the renal parenchyma. The “double-layer sign” were seen in 4 cases of classic type of CMN, and the “intratumor pelvis sign” were seen in 9 cases that include 5 classic, 3 cellular and 1 mixed type of CMN. In the 2 patients who underwent MRI, the scans showed solitary masses. The lesions had hypointense signals on the T1WI sequence and isointensity or slightly lower-intensity signals than the surrounding renal parenchyma on the fluid-sensitive sequences, whereas the lesions showed hyperintense signals on the diffusion-weighted imaging (DWI) sequence. ConclusionsThe imaging manifestations of CMN are closely correlated with the pathological subtype and have certain characteristics. The “double-layer sign” was seen with most classic type CMN, and “intratumor pelvis sign” was seen in 90% cases.

Is hypervascular papillary renal cell carcinoma present?

We aimed to investigate atypical papillary renal cell carcinoma (PRCC) presenting with early contrast enhancement and late washout and to investigate the correlation between the CT attenuation value of the corticomedullary phase (CMP) of contrast-enhanced CT in PRCCs and the endothelial cell counts of these tumors. Twenty-two patients with pathologically confirmed PRCC were enrolled in this study. PRCCs were categorized into 18 typical PRCCs and 4 atypical PRCCs. The CT attenuation value of the lesion in the CMP was measured in the maximal section of the tumor using the region of interest. Microvessel density (MVD) was evaluated as a histopathologic parameter using tissue specimens immunohistochemically stained with an anti-ERG antibody. The CT attenuation value and MVD were compared between atypical and typical PRCCs using the Mann-Whitney U test, where p < 0.05 was considered significant. The correlations between CT attenuation value and MVD were evaluated in all PRCCs using single linear regression analysis. The mean CT attenuation value and the MVD were significantly higher in atypical than in typical PRCCs. Correlation analyses revealed a weak positive correlation between the CT attenuation value and MVD. We confirmed several cases of atypical PRCC that present with early contrast enhancement, such as clear cell renal cell carcinoma. In addition, a positive correlation was found between the CT attenuation value in the CMP of PRCCs and the vascular endothelial cell count.

Effect of phase of enhancement on texture analysis in renal masses evaluated with non-contrast-enhanced, corticomedullary, and nephrographic phase-enhanced CT images.

To compare texture analysis (TA) features of solid renal masses on renal protocol (non-contrast enhanced [NECT], corticomedullary [CM], nephrographic [NG]) CT. A total of 177 consecutive solid renal masses (116 renal cell carcinoma [RCC]; 51 clear cell [cc], 40 papillary, 25 chromophobe, and 61 benign masses; 49 oncocytomas, 12 fat-poor angiomyolipomas) with three-phase CT between 2012 and 2017 were studied. Two blinded radiologists independently assessed tumor heterogeneity (5-point Likert scale) and segmented tumors. TA features (N = 25) were compared between groups and between phases. Accuracy (area under the curve [AUC]) for RCC versus benign and cc-RCC versus other masses was compared. Subjectively, tumor heterogeneity differed between phases (p < 0.01) and between tumors within the same phase (p = 0.03 [NECT] and p < 0.01 [CM, NG]). Inter-observer agreement was moderate to substantial (intraclass correlation coefficient = 0.55-0.73). TA differed in 92.0% (23/25) features between phases (p < 0.05) except for GLNU and f6. More TA features differed significantly on CM (80.0% [20/25]) compared with NG (40.0% [10/25]) and NECT (16.0% [4/25]) (p < 0.01). For RCC versus benign, AUCs of texture features did not differ comparing CM and NG (p > 0.05), but were higher for 20% (5/25) and 28% (7/25) of features comparing CM and NG with NECT (p < 0.05). For cc-RCC versus other, 36% (9/25) and 40% (10/25) features on CM had higher AUCs compared with NECT and NG images (p < 0.05). Texture analysis of renal masses differs, when evaluated subjectively and quantitatively, by phase of CT enhancement. The corticomedullary phase had the highest discriminatory value when comparing masses and for differentiating cc-RCC from other masses. • Subjectively evaluated renal tumor heterogeneity on CT differs by phase of enhancement. • Quantitative CT texture analysis features in renal tumors differ by phases of enhancement with the corticomedullary phase showing the highest number and most significant differences compared with non-contrast-enhanced and nephrographic phase images. • For diagnosis of clear cell RCC, corticomedullary phase texture analysis features had improved accuracy of classification in approximately 40% of features studied compared with non-contrast-enhanced and nephrographic phase images.

Shape and texture-based radiomics signature on CT effectively discriminates benign from malignant renal masses.

Using a radiomics framework to quantitatively analyze tumor shape and texture features in three dimensions, we tested its ability to objectively and robustly distinguish between benign and malignant renal masses. We assessed the relative contributions of shape and texture metrics separately and together in the prediction model. Computed tomography (CT) images of 735 patients with 539 malignant and 196 benign masses were segmented in this retrospective study. Thirty-three shape and 760 texture metrics were calculated per tumor. Tumor classification models using shape, texture, and both metrics were built using random forest and AdaBoost with tenfold cross-validation. Sensitivity analyses on five sub-cohorts with respect to the acquisition phase were conducted. Additional sensitivity analyses after multiple imputation were also conducted. Model performance was assessed using AUC. Random forest classifier showed shape metrics featuring within the top 10% performing metrics regardless of phase, attaining the highest variable importance in the corticomedullary phase. Convex hull perimeter ratio is a consistently high-performing shape feature. Shape metrics alone achieved an AUC ranging 0.64-0.68 across multiple classifiers, compared with 0.67-0.75 and 0.68-0.75 achieved by texture-only and combined models, respectively. Shape metrics alone attain high prediction performance and high variable importance in the combined model, while being independent of the acquisition phase (unlike texture). Shape analysis therefore should not be overlooked in its potential to distinguish benign from malignant tumors, and future radiomics platforms powered by machine learning should harness both shape and texture metrics. • Current radiomics research is heavily weighted towards texture analysis, but quantitative shape metrics should not be ignored in their potential to distinguish benign from malignant renal tumors. • Shape metrics alone can attain high prediction performance and demonstrate high variable importance in the combined shape and texture radiomics model. • Any future radiomics platform powered by machine learning should harness both shape and texture metrics, especially since tumor shape (unlike texture) is independent of the acquisition phase and more robust from the imaging variations.

A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma.

This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤ p ≤ 0.0051) and the subjective findings model (0.0006 ≤ p ≤ 0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation.

CT texture analysis for the differentiation of papillary renal cell carcinoma subtypes.

The objective of this study was to investigate whether computed tomography texture analysis can be used to differentiate papillary renal cell carcinoma (PRCC) subtypes. Sixty-two PRCC tumors were retrospectively evaluated, with 30 type 1 tumors and 32 type 2 tumors. Texture parameters quantified from three-phase contrast-enhanced CT images were compared with least absolute shrinkage and selection operator (LASSO) regression. Receiver operating characteristic (ROC) analysis was performed, and the area under the ROC curve (AUC) was calculated for each parameter. The selected texture parameters of each phase were used to generate support vector machine (SVM) classifiers. Decision curve analysis (DCA) of the classification was performed. The two texture parameters with the top two AUC values were - 333-7 Correlation (AUC = 0.772) and 45-7 Entropy (AUC = 0.753) in the corticomedullary phase, 333-4 Correlation (AUC = 0.832) and 45-7 Entropy (AUC = 0.841) in the nephrographic phase, and 135-7 Entropy (AUC = 0.858) and - 333-1 InformationMeasureCorr2 (AUC = 0.849) in the excretory phase. Entropy and Correlation have a high correlation with the two types of PRCC and are increased in type 2 PRCC. A model incorporating the texture parameters with the top two AUC values in each phase produced an AUC of 0.922 with an accuracy of 84% (sensitivity = 89% and specificity = 80%). The nephrographic-phase model and the model combining the texture parameters of the three phases can differentiate the two types with the largest net benefit. Computed tomography texture analysis can be used to distinguish type 2 PRCC from type 1 with high accuracy, which may be clinically important.

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

Small renal masses (≤ 4cm): differentiation of oncocytoma from renal clear cell carcinoma using ratio of lesion to cortex attenuation and aorta-lesion attenuation difference (ALAD) on contrast-enhanced CT.

We investigate the use of ratio of lesion to cortex (L/C) attenuation and aorta-lesion attenuation difference (ALAD) on multiphase contrast-enhanced CT to help distinguish oncocytoma from clear cell RCC in small renal masses (diameter < 4cm). We retrospectively identified 76 patients that undergo CT before surgery for a suspicious small renal mass between January 2014 and December 2018 with pathological diagnosis of 21 oncocytomas (ROs), 25 clear cell RCCs, 7 chromophobe RCCs, 7 papillary RCCs, 7 multilocular cystic RCCs, 7 angiomyolipomas and 2 leiomyomas. CT attenuation values were obtained for the tumor, the normal renal cortex and the aorta, placing a circular region of interest (ROI) in the same slice by two radiologists, independently. In the corticomedullary phase, ROs showed isodense enhancement to the renal cortex (ratio L/C 0.92 ± 0.12), while clear cell RCCs appeared hypodense to the renal cortex (ratio L/C 0.69 ± 0.20; p < 0.01) with an accuracy of 80% for diagnosing RO. In nephrographic phase, the ratio L/C attenuation was lower than the corticomedullary phase in ROs (0.78 ± 0.11) showing an early washout pattern, while the ratio L/C was similar to the corticomedullary phase in clear cell RCCs (0.69 ± 0.13; p = 0.025, with an accuracy of 65% for diagnosing RO). The ratio L/C attenuation showed considerable overlap between ROs and clear cell RCCs in the excretory phase (p = 0.27). Mean ALAD values in the nephrographic phase were 21.95 ± 16.24 for ROs and 36.96 ± 30.53 for clear cell RCCs (p = 0.049). The ratio L/C attenuation in corticomedullary phase may be useful to differentiate RO from clear cell RCC.

MP21-09 A COMPARISON OF ESTIMATION METHODS FOR RENAL TUMOR CONTACT SURFACE AREA

You have accessJournal of UrologyKidney Cancer: Localized: Surgical Therapy III (MP21)1 Apr 2020MP21-09 A COMPARISON OF ESTIMATION METHODS FOR RENAL TUMOR CONTACT SURFACE AREA Nick Heller*, Arveen Kalapara, Sarah Peterson, Matthew Peterson, Makinna Oestreich, Bethany Stai, Sean McSweeney, Jack Rickman, Paul Blake, Ranveer Vasdev, Zach Edgerton, Resha Tejpaul, Subodh Regmi, Nikolaos Papanikolopoulos, and Christopher Weight Nick Heller*Nick Heller* More articles by this author , Arveen KalaparaArveen Kalapara More articles by this author , Sarah PetersonSarah Peterson More articles by this author , Matthew PetersonMatthew Peterson More articles by this author , Makinna OestreichMakinna Oestreich More articles by this author , Bethany StaiBethany Stai More articles by this author , Sean McSweeneySean McSweeney More articles by this author , Jack RickmanJack Rickman More articles by this author , Paul BlakePaul Blake More articles by this author , Ranveer VasdevRanveer Vasdev More articles by this author , Zach EdgertonZach Edgerton More articles by this author , Resha TejpaulResha Tejpaul More articles by this author , Subodh RegmiSubodh Regmi More articles by this author , Nikolaos PapanikolopoulosNikolaos Papanikolopoulos More articles by this author , and Christopher WeightChristopher Weight More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000854.09AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Renal tumor Contact Surface Area (CSA) is a sensible predictor for Partial Nephrectomy (PN) complexity. Three methods for estimating CSA have been proposed, but these have only been studied independently. The objective of this work is to critically analyze each of these methods on the same cohort, comparing their predictive power and noting cases in which some methods break down. We conclude by discussing each method’s utility and making recommendations for which to use in practice. METHODS: We conducted a retrospective review of patients who underwent PN at our center between 2010 and 2018. We excluded cases which did not have imaging available in the corticomedullary phase as well as those with tumor thrombus. This turned up 188 patients from whom we randomly sampled 139 for our study. Students computed CSA estimates based on the three proposed methods (from 2014, 2016, and 2019) for each case, while also collecting ischemia time and Estimated Blood Loss (EBL). Multiple linear regression was used for analysis. RESULTS: We identified failure modes for the 2014 and 2016 methods, but the 2019 method of semantic segmentation + voxel counting was highly robust. In particular, both the 2014 and 2016 methods utilize a “proportion interparenchymal” estimate based on the tumor’s largest axial view, and in cases where (i) no adjacent parenchyma is shown, or (ii) the boundary between tumor and parenchyma is flat or concave, these methods severely underestimate CSA. A further third case in which this axial view shows disjoint parenchyma on both sides of the tumor introduces ambiguity when measuring depth of penetration for the 2016 method. However, since these cases are rare, the estimates that were highly correlated and equivalently useful as an addition to the RENAL score. When regressing over ischemia time with RENAL as a continuous predictor, the 2014, 2016, and 2019 methods took p-values of 6.85e-5, 1.00e-3, and 3.37e-3 respectively. The same test for EBL produced p-values of 0.0210, 0.0280, and 0.0265 respectively. CONCLUSIONS: Our data suggest that no matter the estimation method, CSA makes a useful addition to the RENAL score for characterizing PN complexity. The 2019 method, while the most robust, requires full semantic segmentation of the tumor and affected kidney, which currently requires significantly more manual effort than the first two methods. Machine learning has recently shown impressive performance on automating this semantic segmentation, so fully automated CSA by the 2019 method is likely not far away, but until then, we recommend using the first proposed method of Leslie et al. since it is slightly more robust. Source of Funding: NIH GRANT:R01CA225435 © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e321-e321 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Nick Heller* More articles by this author Arveen Kalapara More articles by this author Sarah Peterson More articles by this author Matthew Peterson More articles by this author Makinna Oestreich More articles by this author Bethany Stai More articles by this author Sean McSweeney More articles by this author Jack Rickman More articles by this author Paul Blake More articles by this author Ranveer Vasdev More articles by this author Zach Edgerton More articles by this author Resha Tejpaul More articles by this author Subodh Regmi More articles by this author Nikolaos Papanikolopoulos More articles by this author Christopher Weight More articles by this author Expand All Advertisement PDF downloadLoading ...

Preoperative differentiation of chromophobe renal cell carcinoma from oncocytoma: Aorta-lesion-attenuation difference on multiphasic multidetector computed tomography.

627 Background: Differentiation of benign oncocytoma from chromophobe renal cell carcinoma (chRCC) remains a challenge. This study aimed to investigate whether Aorta-lesion-attenuation difference (ALAD) on multiphasic multidetector CT scan (MDCT) can aid to distinguish oncocytoma from chRCC. Methods: This IRB-approved, HIPAA-compliant study consisted MDCT of 111 patients with pathologically proven chRCC (N: 37) or oncocytoma (N: 74). Regions of interest (ROIs) were placed over the renal mass and Aorta on the same axial MDCT slice of un-enhanced (UE), corticomedullary (CM), nephrographic (NP) and excretory (EX) phases. ROIs were devoid of calcification, necrosis and hemorrhage in lesion or atherosclerotic plaque in Aorta. The difference between Aorta and renal mass Hounsfield unit was calculated in all available phases. SPSS v.18 was used to draw receiver-operating characteristic (ROC) curve to calculate accuracy and optimal cutoff values for differentiation of chRCC from oncocytoma. Results: Mean ALAD was significantly higher in chRCC compared to oncocytoma in CM(P: 0.04), NP (P&lt;0.01) and EX (P&lt;0.01) phases. The area under the curves for ALAD in UE, CM, NP and EX phases were 0.47 (95% CI: 0.36- 0.59, p: 0.68), 0.64 (95% CI: 0.51-0.77, p: 0.03), 0.87 (95% CI: 0.79- 0.95, p&lt; 0.01) and 0.80 (0.70-0.90, P&lt;0.001), respectively. ALAD threshold of 19 in nephrographic phase had sensitivity and specificity of 79% and 78%, respectively, for differentiation of chRCC from oncocytoma. Conclusions: ALAD measured in the nephrogenic phase showed the highest accuracy for differentiation of chRCC from oncocytoma on multidetector CT scan. If validated prospectively, ALAD may act as a useful imaging biomarker to distinguish chRCC from oncocytoma.

Computed tomography imaging characteristics of clear cell papillary renal cell carcinoma.

ABSTRACTPurpose:Clear cell papillary (CCP) renal cell carcinoma (RCC) is a new subtype of RCC that was formally recognized by the International Society of Urological Pathology Vancouver Classification of Renal Neoplasia in 2013. Subsequently, CCP RCC was added to the 2016 World Health Organization Classification of Tumors of the Urinary System and Male Genital Organs. In this study, we retrospectively investigated the computed tomography (CT) findings of pathologically diagnosed CCP RCC.Materials and Methods:This study included 12 patients pathologically diagnosed with CCP RCC at our institution between 2015 and 2017. We reviewed the patient's CT data and analyzed the characteristics.Results:Nine solid masses and 3 cystic masses with a mean tumor size of 22.7±9.2mm were included. Solid masses exhibited slight hyper-density on unenhanced CT with a mean value of 34±6 Hounsfield units (HU), good enhancement in the corticomedullary phase with a mean of 195±34HU, and washout in the nephrogenic phase with a mean of 133±29HU. The walls of cystic masses enhanced gradually during the corticomedullary and nephrogenic phases. Solid and cystic masses were preoperatively diagnosed as clear cell RCC and cystic RCC, respectively.Conclusions:The CT imaging characteristics of CCP RCCs could be categorized into either the solid or cystic type. These masses were diagnosed radiologically as clear cell RCC and cystic RCC, respectively.

Predicting the ISUP grade of clear cell renal cell carcinoma with multiparametric MR and multiphase CT radiomics.

To investigate externally validated magnetic resonance (MR)-based and computed tomography (CT)-based machine learning (ML) models for grading clear cell renal cell carcinoma (ccRCC). Patients with pathologically proven ccRCC in 2009-2018 were retrospectively included for model development and internal validation; patients from another independent institution and The Cancer Imaging Archive dataset were included for external validation. Features were extracted from T1-weighted, T2-weighted, corticomedullary-phase (CMP), and nephrographic-phase (NP) MR as well as precontrast-phase (PCP), CMP, and NP CT. CatBoost was used for ML-model investigation. The reproducibility of texture features was assessed using intraclass correlation coefficient (ICC). Accuracy (ACC) was used for ML-model performance evaluation. Twenty external and 440 internal cases were included. Among 368 and 276 texture features from MR and CT, 322 and 250 features with good to excellent reproducibility (ICC ≥ 0.75) were included for ML-model development. The best MR- and CT-based ML models satisfactorily distinguished high- from low-grade ccRCCs in internal (MR-ACC = 73% and CT-ACC = 79%) and external (MR-ACC = 74% and CT-ACC = 69%) validation. Compared to single-sequence or single-phase images, the classifiers based on all-sequence MR (71% to 73% in internal and 64% to 74% in external validation) and all-phase CT (77% to 79% in internal and 61% to 69% in external validation) images had significant increases in ACC. MR- and CT-based ML models are valuable noninvasive techniques for discriminating high- from low-grade ccRCCs, and multiparameter MR- and multiphase CT-based classifiers are potentially superior to those based on single-sequence or single-phase imaging. • Both the MR- and CT-based machine learning models are reliable predictors for differentiating high- from low-grade ccRCCs. • ML models based on multiparameter MR sequences and multiphase CT images potentially outperform those based on single-sequence or single-phase images in ccRCC grading.

Can whole-tumor radiomics-based CT analysis better differentiate fat-poor angiomyolipoma from clear cell renal cell caricinoma: compared with conventional CT analysis?

This study aimed to discriminate fat-poor angiomyolipoma (fp-AML) from clear cell renal cell carcinoma (ccRCC) by constructing radiomics-based logistic classifiers in comparison with conventional computed tomography (CT) analysis at three CT phases. Twenty-two fp-AML patients and 62 ccRCC patients who were pathologically identified were enrolled in this study, and underwent three-phase (unenhanced phase, UP; corticomedullary phase, CMP; nephrographic phase, NP) CT examinations. Whole-tumor regions of interest (ROIs) were contoured in ITK software by two radiologists. Radiomic features were dimensionally reduced by means of ANOVA + MW, correlation analysis, and LASSO. Four radiomics logistic classifiers including the UP group, CMP group, NP group, and sum group were built, and the radiomic scores (rad-scores) were calculated. After collecting the qualitative and quantitative conventional CT characteristics, the conventional CT analysis logistic classifier and the radiomics-based logistic classifier were constructed. The receiver operating characteristic curve (ROC) was constructed to evaluate the validity of each classifier. The area under curve (AUC) of the conventional CT analysis logistic classifier including angular interface, cyst degeneration, and pseudocapsule was 0.935 (95% CI 0.860-0.977). Regarding logistic classifiers for radiomics analysis, the AUCs of the UP group were 0.950 (95% CI 0.895-1.000) and 0.917 (95% CI 0.801-1.000) in the training set and testing set, respectively, which were higher than those of the CMP and NP groups. The AUCs of the sum group were observed to be the highest. The top three selected features for the UP and sum groups belonged to GLCM parameters and histogram parameters. The radiomics-based logistic classifier encompassed cyst degeneration, pseudocapsule, and sum rad-score, and the AUC of the model was 0.988 (95% CI 0.935-1.000). Whole-tumor radiomics-based CT analysis is superior to conventional CT analysis in the differentiation of fp-AML from ccRCC. Cyst degeneration, pseudocapsule, and sum rad-score are the most significant factors. The radiomics analysis of the UP group shows a higher AUC than that of the CMP and NP groups.

Differentiation of Small (≤ 4 cm) Renal Masses on Multiphase Contrast-Enhanced CT by Deep Learning.

OBJECTIVE. This study evaluated the utility of a deep learning method for determining whether a small (≤ 4 cm) solid renal mass was benign or malignant on multiphase contrast-enhanced CT. MATERIALS AND METHODS. This retrospective study included 1807 image sets from 168 pathologically diagnosed small (≤ 4 cm) solid renal masses with four CT phases (unenhanced, corticomedullary, nephrogenic, and excretory) in 159 patients between 2012 and 2016. Masses were classified as malignant (n = 136) or benign (n = 32). The dataset was randomly divided into five subsets: four were used for augmentation and supervised training (48,832 images), and one was used for testing (281 images). The Inception-v3 architecture convolutional neural network (CNN) model was used. The AUC for malignancy and accuracy at optimal cutoff values of output data were evaluated in six different CNN models. Multivariate logistic regression analysis was also performed. RESULTS. Malignant and benign lesions showed no significant difference of size. The AUC value of corticomedullary phase was higher than that of other phases (corticomedullary vs excretory, p = 0.022). The highest accuracy (88%) was achieved in corticomedullary phase images. Multivariate analysis revealed that the CNN model of corticomedullary phase was a significant predictor for malignancy compared with other CNN models, age, sex, and lesion size. CONCLUSION. A deep learning method with a CNN allowed acceptable differentiation of small (≤ 4 cm) solid renal masses in dynamic CT images, especially in the corticomedullary image model.

Texture analysis and machine learning algorithms accurately predict histologic grade in small (&lt; 4 cm) clear cell renal cell carcinomas: a pilot study

To predict the histologic grade of small clear cell renal cell carcinomas (ccRCCs) using texture analysis and machine learning algorithms. Fifty-two noncontrast (NC), 26 corticomedullary (CM) phase, and 35 nephrographic (NG) phase CTs of small (< 4cm) surgically resected ccRCCs were retrospectively identified. Surgical pathology classified the tumors as low- or high-Fuhrman histologic grade. The axial image with the largest cross-sectional tumor area was exported and segmented. Six histogram and 31 texture (gray-level co-occurrences (GLC) and gray-level run-lengths (GLRL)) features were calculated for each tumor in each phase. T testing compared feature values in low- and high-grade ccRCCs, with a (Benjamini-Hochberg) false discovery rate of 10%. Area under the receiver operating curve (AUC) was calculated for each feature to assess prediction of low- and high-grade ccRCCs in each phase. Histogram, texture, and combined histogram and texture data sets were used to train and test four algorithms (k-nearest neighbor (KNN), support vector machine (SVM), random forests, and decision tree) with tenfold cross-validation; AUCs were calculated for each algorithm in each phase to assess prediction of low- and high-grade ccRCCs. Zero, 23, and 0 features in the NC, CM, and NG phases had statistically significant differences between low and high-grade ccRCCs. CM histogram skewness and GLRL short run emphasis had the highest AUCs (0.82) in predicting histologic grade. All four algorithms had the highest AUCs (0.97) predicting histologic grade using CM histogram features. The algorithms' AUCs decreased using histogram or texture features from NC or NG phases. The histologic grade of small ccRCCs can be accurately predicted with machine learning algorithms using CM histogram features, which outperform NC and NG phase image data.

Clear Cell Papillary Renal Cell Carcinoma: A Recent Entity With Distinct Imaging Patterns.

OBJECTIVE. Clear cell papillary renal cell carcinoma (RCC), an entity with strikingly indolent behavior, recently was added to the World Health Organization classification of renal tumors and represents the fourth most common histologic type of renal cell carcinoma. This article aims to describe the imaging features of clear cell papillary RCC along with its clinical and pathologic characteristics. MATERIALS AND METHODS. This retrospective study consisted of 27 patients with 44 clear cell papillary RCC tumors. The inclusion criteria were a pathologically proven clear cell papillary RCC and the availability of preoperative imaging including at least CT or MRI. Two experienced radiologists performed the imaging analysis independently. RESULTS. Patients (mean age, 62 years old) presented with renal failure in 26% of cases, and four had a tumor-predisposing disease. Multiple clear cell papillary RCC tumors occurred in 5 of the 27 patients. Two imaging patterns were recognizable. Solid clear cell papillary RCC (n = 23, 52%) presented as heterogeneous tumors with minor cystic changes (74%) and rarely exhibited calcifications (10%). All solid tumors showed hyperintensity on T2-weighted images compared with renal cortex and maximal enhancement on corticomedullary phase with a delayed washout. Cystic clear cell papillary RCC (n = 21, 48%) were classified as Bosniak IV (57%), III (33%), or IIF (10%), with a predominant unilocular pattern (76%). Pathologic stage according to TNM classification was mostly pT1a and low grade on nucleolar grade. All patients were alive at the date of last follow-up after treatment with no metastasis or recurrence. CONCLUSION. Clear cell papillary RCC exhibits two imaging patterns including cystic and solid in almost equal proportion. Imaging characteristics of solid clear cell papillary RCC including high signal T2 intensity and early arterial enhancement are unexpectedly distinct from papillary RCC and very similar to clear cell RCC.