Nephrogenic Phase Research Articles

Can We Predict the Grade of Clear Cell Renal Cell Carcinoma from Houns-Field Unit of Renal Lesion on Computerized Tomography Scan, a Retrospective Cross-Sectional Study.

Renal cell carcinoma (RCC) is a type of urological malignancy that affects approximately 2% of the global population. Imaging modalities, especially computed tomography (CT) scanning, play a critical role in diagnosing RCC. In this study, we investigated whether there is a relationship between tumour grade of clear cell RCC and HU values of renal lesions on CT scan performed before operation. We conducted a retrospective analysis of 123 patients who underwent radical or partial (open or laparoscopic) nephrectomy for clear cell RCC between January 2017 and January 2021. Post-operation histopathological grades were recorded according to World Health Organization (WHO)/International Society of Urological Pathology (ISUP) 2016 grading system and divided into low grade (includes grade 1 and 2) and high grade (grade 3 and 4), and their links to age, sex, smoking habits, tumour size, and HUs of renal lesions were evaluated. The mean age of the patients studied was 63.02 years old. About 56.9% of the patients were low grade (grade 1 or grade 2), while 43.1% were high grade (grade 3 or 4). The mean tumour size was 6.31 cm. There were no significant differences in tumour grade according to age, sex, or smoking habits. We found a significant relation between tumour grade and HU in the pre-contrast and nephrogenic phases, with p values of 0.001 and 0.037, respectively. On the other hand, there was no significant relation linking the tumour grade to the difference in HU between these phases, where there was a p value of 0.641. HU in the pre-contrast and nephrogenic phases in addition to tumour size on CT scan have a significant relation to clear cell RCC grade.

The application of machine learning based on computed tomography images in the identification of renal tumors in children.

The clinical manifestations of Wilms tumor and non-Wilms tumor in children are similar, and the only way to confirm the diagnosis is by postoperative pathology. Computed tomography (CT) is one of the main methods for preoperative diagnosis of the two, but it is also difficult to distinguish because it is easily affected by the subjective influence and the experience of the radiologists. The CT images of 82 children with renal tumors admitted to the Department of Pediatric Urology, Shandong Provincial Hospital from January 2011 to March 2022 were retrospectively analyzed. First, we drew the two-dimensional (2D) region of interest (ROI) of the largest cross-section on the corticomedullary phase (CMP) and nephrogenic phase (NP) images, and extracted seven types of 107 features in the ROI. Then, the texture features with similarity greater than 95% and repetition less than 90% were screened out, and the remaining texture features were further screened by analysis of variance (ANOVA) and recursive feature elimination (RFE). Finally, 15 texture feature were used to build the machine learning (ML) models. We used the synthetic minority oversampling technique (SMOTE) and 10-fold cross-validation to build ML models and verified them in the training, testing, and internal validation sets. The area under the receiver-operating characteristic curve (AUC) and calibration curve were used to evaluate the diagnostic performance. We collected 77 CMP and 81 NP images, which were randomly divided into the training set and the testing set according to the ratio of 7:3. In the internal validation of CMP, the Mean-PCC-ANOVA-5-AE pipeline model achieved the highest AUC 0.792 [95% confidence interval (CI): 0.653-0.930], and its accuracy (ACC), sensitivity (SEN), and specificity (SPE) were 0.833, 0.539 and 0.927, respectively. Correspondingly, in NP, the Mean-PCC-ANOVA-2-LR pipeline model achieved the highest AUC 0.655 (95% CI: 0.485-0.82) in the internal validation. The ACC, SEN, and SPE were 0.696, 0.539, and 0.744, respectively. The ML models based on CT images have good diagnostic efficiency in differentiating Wilms tumors from non-Wilms tumors in children.

CT radiomics for differentiating fat poor angiomyolipoma from clear cell renal cell carcinoma: Systematic review and meta-analysis.

Differentiation of fat-poor angiomyolipoma (fp-AMLs) from renal cell carcinoma (RCC) is often not possible from just visual interpretation of conventional cross-sectional imaging, typically requiring biopsy or surgery for diagnostic confirmation. However, radiomics has the potential to characterize renal masses without the need for invasive procedures. Here, we conducted a systematic review on the accuracy of CT radiomics in distinguishing fp-AMLs from RCCs. We conducted a search using PubMed/MEDLINE, Google Scholar, Cochrane Library, Embase, and Web of Science for studies published from January 2011-2022 that utilized CT radiomics to discriminate between fp-AMLs and RCCs. A random-effects model was applied for the meta-analysis according to the heterogeneity level. Furthermore, subgroup analyses (group 1: RCCs vs. fp-AML, and group 2: ccRCC vs. fp-AML), and quality assessment were also conducted to explore the possible effect of interstudy differences. To evaluate CT radiomics performance, the pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were assessed. This study is registered with PROSPERO (CRD42022311034). Our literature search identified 10 studies with 1456 lesions in 1437 patients. Pooled sensitivity was 0.779 [95% CI: 0.562-0.907] and 0.817 [95% CI: 0.663-0.910] for groups 1 and 2, respectively. Pooled specificity was 0.933 [95% CI: 0.814-0.978]and 0.926 [95% CI: 0.854-0.964] for groups 1 and 2, respectively. Also, our findings showed higher sensitivity and specificity of 0.858 [95% CI: 0.742-0.927] and 0.886 [95% CI: 0.819-0.930] for detecting ccRCC from fp-AML in the unenhanced phase of CT scan as compared to the corticomedullary and nephrogenic phases of CT scan. This study suggested that radiomic features derived from CT has high sensitivity and specificity in differentiating RCCs vs. fp-AML, particularly in detecting ccRCCs vs. fp-AML. Also, an unenhanced CT scan showed the highest specificity and sensitivity as compared to contrast CT scan phases. Differentiating between fp-AML and RCC often is not possible without biopsy or surgery; radiomics has the potential to obviate these invasive procedures due to its high diagnostic accuracy.

RADIOLOGIC CHARACTERISTICS OF COMPUTERIZED TOMOGRAPHY ATTENUATION IN RENAL CELL CARCINOMA

Background: Renal cell carcinoma (RCC) is the most common kidney cancer in adults. Computed Tomography (CT) with contrast study is used to diagnose RCC. The enhancement in the nephrogenic phase more than 15 Hounsfield units (HU) is suspected of RCCs. However, this threshold HU shows 15-20% false positive results for RCCs.
 Objectives: This study aimed to determine RCC enhancement in CT that was below the standard threshold and to analyze the attenuation range of RCCs in noncontrast CT. Methods: Patients with pathological RCC and undergoing CT with contrast study were retrospectively reviewed. An average of attenuation values of three regions of interest (ROI) were measured in noncontrast and nephrogenic phases, by avoiding foci of calcification and peritumoral region. ROI values were calculated for enhancement and range of attenuation values in the noncontrast CT.
 Results: A total of 152 pathologically RCCs were included in the study. Mean ± SD attenuation values were 32.54 ± 8.02 HU (range 13.3-57.23 HU) and 71.26 ± 33.1 HU (range 16.87-202.8 HU) for noncontrast and contrast CT, respectively. Thirty-one (20.4%) of RCCs did not reach 15 HU enhancement. Using multivariate analysis, significant differences among subtypes (p<0.001) and renal mass less than 7 cm (p<0.001) were observed. In noncontrast CT, using a range of 20-60 HU, 129 (84.9%) RCCs were entirely within this range. To improve the accuracy of RCC diagnosis, the combined use of both non-contrast attenuation group (<20 HU and >20 HU) and enhancement >15 HU could increase the accuracy to 96.7%.
 Conclusion: One-fifth of RCCs did not reach the standard enhancement threshold that were mostly found in nonclear cell subtype. Especially, when the mass was larger than 7 cm or involved nonclear cell RCCs, the enhancement threshold >15 HU must be carefully used for diagnosis. Using a noncontrast phase regardless HU combined with enhancement >15 HU could improve the accuracy of RCC diagnosis.

Correlation between renal ablation zone in contrast-enhanced CT and non-enhanced MRI during the early period following percutaneous cryoablation

PurposeTo retrospectively evaluate and correlate the contrast-enhanced computed tomography (CECT) and non-enhanced magnetic resonance imaging (MRI) during the early period following renal cryoablation.Materials and methodsBoth dynamic CECT and non-enhanced MRI were performed within 4 days following cryoablation in 34 renal tumors in 33 patients. The renal volumes of the unenhanced regions on dynamic CECT (nephrogenic phase, 4 mm thickness) and the regions with signal intensity changes on non-enhanced MRI (fat-suppressed T2-weighted image, 4 mm thickness) were evaluated. Fusion images of the axial, coronal, and sagittal sections of CECT and MRI images were created from the maximum cross-section of the renal tumor, and the match score of each image was visually evaluated on a 5-point scale.ResultsThe mean renal volume of the unenhanced regions on CECT and those with signal intensity changes on non-enhanced MRI following cryoablation were 29.5 ± 19.9 cm3 (range, 4.3–97.4 cm3) and 30.7 ± 19.8 cm3 (range, 6.7–94.0 cm3), respectively; the difference between them was –1.17 cm3 (95% confidence interval [CI] –2.74, 0.40, P = 0.139). The Pearson’s product-moment correlation coefficient (r = 0.975; 95% CI, 0.951, 0.988; P < 0.0001) showed a strong correlation between the volumes. The average match score between CECT and non-enhanced MRI was as high as 4.5 ± 0.5 points (radiologist 1, 4.3 ± 0.5; radiologist 2, 4.7 ± 0.5). Local tumor control rate was 94.1% (32/34 tumors) and recurrence-free survival rate was 82.0% (95% CI: 64.2%, 91.5%) at 5 years.ConclusionsThe region with signal intensity changes on non-enhanced MRI was strongly correlated with the unenhanced region on CECT during the early period following renal cryoablation.

MP41-19 PHASE I NCT03738488. FEASIBLE 3D PRINTED MODELS OF RENAL CELL CARCINOMA WITH VENOUS THROMBUS EXTENSION FOR SURGICAL PLANNING AND SIMULATION

You have accessJournal of UrologyCME1 May 2022MP41-19 PHASE I NCT03738488. FEASIBLE 3D PRINTED MODELS OF RENAL CELL CARCINOMA WITH VENOUS THROMBUS EXTENSION FOR SURGICAL PLANNING AND SIMULATION Ines Rivero Belenchon, Carmen Belen Congregado Ruiz, Gorka Gomez Ciriza, Victoria Gomez Dos Santos, Cristina Asensio, Cristina Gonzalez Gordaliza, Ignacio Osman Garcia, Jose Manuel Conde Sanchez, Francisco Javier Burgos Revilla, and Rafael Antonio Medina Lopez Ines Rivero BelenchonInes Rivero Belenchon More articles by this author , Carmen Belen Congregado RuizCarmen Belen Congregado Ruiz More articles by this author , Gorka Gomez CirizaGorka Gomez Ciriza More articles by this author , Victoria Gomez Dos SantosVictoria Gomez Dos Santos More articles by this author , Cristina AsensioCristina Asensio More articles by this author , Cristina Gonzalez GordalizaCristina Gonzalez Gordaliza More articles by this author , Ignacio Osman GarciaIgnacio Osman Garcia More articles by this author , Jose Manuel Conde SanchezJose Manuel Conde Sanchez More articles by this author , Francisco Javier Burgos RevillaFrancisco Javier Burgos Revilla More articles by this author , and Rafael Antonio Medina LopezRafael Antonio Medina Lopez More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002607.19AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Renal Cell Cancer (RCC) accounts for 3-5% of all adults malignancies, and up to 10% are presented with venous thrombus extension (VTE). This worsens prognosis and represents a therapeutic challenge. 3D biomodels are printed copies of patients´ radiological images with visual and tactile components that enhance understanding of anatomy and may improve surgical planning, communication and training. This is a Multicenter Clinical Trial (NCT03738488), which aims to assess the efficacy and efficiency of surgery planning with 3D in RCC with VTE. The objective of the phase I is to obtain a feasible, affordable, accurate and suitable for surgical simulation 3D model. METHODS: A CT image in early arterial and nephrogenic phase was obtained. ITK-snap® and VirSSPA Software® were used for segmentation. The resulting 3D mesh was processed with MeshMixer®. Multiple models were printed using different 3D printers and materials. We evaluated: material, scale, thickness, accuracy, suitability for surgery, cost and printing time. 6 urologists completed a satisfaction questionnaire. RESULTS: 4 models were discarded (Table 1). The selected one was printed with BQ Witbox FDM printer in polyurethane filament with a 0.8 mm thickness and 100% scale. All anatomical structures could be correctly identified with a good accuracy compared to the CT (<5 mm deviation) and the surgery could be performed on it. Model cost was 15€ and whole processing and printing time 48 h. 100% of urologists thought that the obtained 3D model could be useful for surgery planning and simulation. CONCLUSIONS: A feasible and affordable 3D printed model of RCC with VTE was obtained. It was accurate compared to the CT and suitable for surgery simulation. Urologists thought it could be useful for surgery planning and simulation. Source of Funding: Funded by Pfizer © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e728 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ines Rivero Belenchon More articles by this author Carmen Belen Congregado Ruiz More articles by this author Gorka Gomez Ciriza More articles by this author Victoria Gomez Dos Santos More articles by this author Cristina Asensio More articles by this author Cristina Gonzalez Gordaliza More articles by this author Ignacio Osman Garcia More articles by this author Jose Manuel Conde Sanchez More articles by this author Francisco Javier Burgos Revilla More articles by this author Rafael Antonio Medina Lopez More articles by this author Expand All Advertisement PDF DownloadLoading ...

Using Machine Learning to Identify Intravenous Contrast Phases on Computed Tomography

PurposeThe purpose of the present work is to demonstrate the application of machine learning (ML) techniques to automatically identify the presence and physiologic phase of intravenous (IV) contrast in Computed Tomography (CT) scans of the Chest, Abdomen and Pelvis. Materials and MethodsTraining, testing and validation data were acquired from a dataset of 82,690 chest and abdomen CT examinations performed at 17 different institutions. Free text in DICOM metadata was utilized as weak labels for semi-supervised classification training. Contrast phase identification was approached as a classification task, using a 12-layer CNN and ResNet18 with four contrast-phase output. The model was reformulated to fit a regression task aimed to predict actual seconds from time of IV contrast administration to series image acquisition. Finally, transfer learning was used to optimize the model to predict contrast presence on CT Chest. ResultsBy training based on labels inferred from noisy, free text DICOM information, contrast phase was predicted with 93.3% test accuracy (95% CI: 89.3%, 96.6%) . Regression analysis resulted in delineation of early vs late arterial phases and a nephrogenic phase in between the portal venous and delayed excretory phase. Transfer learning applied to Chest CT achieved an AUROC of 0.776 (95% CI: 0.721, 0.832) directly using the model trained for abdomen CT and 0.999 (95% CI: 0.998, 1.000) by fine-tuning. ConclusionsThe presence and phase of contrast on CT examinations of the Abdomen-pelvis accurately and automatically be ascertained by a machine learning algorithm. Transfer learning applied to CT Chest achieves high precision with as little as 100 labeled samples.

Deep Learning Algorithm for Fully Automated Detection of Small (≤4 cm) Renal Cell Carcinoma in Contrast-Enhanced Computed Tomography Using a Multicenter Database.

Renal cell carcinoma (RCC) is often found incidentally in asymptomatic individuals undergoing abdominal computed tomography (CT) examinations. The purpose of our study is to develop a deep learning-based algorithm for fully automated detection of small (≤4 cm) RCCs in contrast-enhanced CT images using a multicenter database and to evaluate its performance. For the algorithmic detection of RCC, we retrospectively selected contrast-enhanced CT images of patients with histologically confirmed single RCC with a tumor diameter of 4 cm or less between January 2005 and May 2020 from 7 centers in the Japan Medical Image Database. A total of 453 patients from 6 centers were selected as dataset A, and 132 patients from 1 center were selected as dataset B. Dataset A was used for training and internal validation. Dataset B was used only for external validation. Nephrogenic phase images of multiphase CT or single-phase postcontrast CT images were used. Our algorithm consisted of 2-step segmentation models, kidney segmentation and tumor segmentation. For internal validation with dataset A, 10-fold cross-validation was applied. For external validation, the models trained with dataset A were tested on dataset B. The detection performance of the models was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). The mean ± SD diameters of RCCs in dataset A and dataset B were 2.67 ± 0.77 cm and 2.64 ± 0.78 cm, respectively. Our algorithm yielded an accuracy, sensitivity, and specificity of 88.3%, 84.3%, and 92.3%, respectively, with dataset A and 87.5%, 84.8%, and 90.2%, respectively, with dataset B. The AUC of the algorithm with dataset A and dataset B was 0.930 and 0.933, respectively. The proposed deep learning-based algorithm achieved high accuracy, sensitivity, specificity, and AUC for the detection of small RCCs with both internal and external validations, suggesting that this algorithm could contribute to the early detection of small RCCs.

MP61-02 DIFFERENTIATION OF RENAL MASS HISTOLOGY AND GRADE UTILIZING DUAL-ENERGY COMPUTED TOMOGRAPHY

You have accessJournal of UrologyKidney Cancer: Epidemiology & Evaluation/Staging/Surveillance III (MP61)1 Sep 2021MP61-02 DIFFERENTIATION OF RENAL MASS HISTOLOGY AND GRADE UTILIZING DUAL-ENERGY COMPUTED TOMOGRAPHY Rishi Sekar, Giuseppe Toia, Achille Mileto, and George Schade Rishi SekarRishi Sekar More articles by this author , Giuseppe ToiaGiuseppe Toia More articles by this author , Achille MiletoAchille Mileto More articles by this author , and George SchadeGeorge Schade More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002101.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Dual-Energy Computed Tomography (DECT) has been shown to more accurately characterize renal mass vascularity, however its ability to differentiate tumor subtypes remains unclear. Herein, we investigate the role of DECT and iodine concentration as a derived imaging biomarker in predicting tumor histology and Fuhrman Nuclear Grade (FNG). METHODS: Our institutional database was queried for patients who underwent contrast enhanced DECT to characterize renal masses with subsequent pathologic diagnosis within our institution from 2018 to present. Tumor histology and FNG were obtained from internal pathologic evaluation. Tumor enhancement was measured in Hounsfield Units (HU) using standard techniques. Tumor iodine concentration (mg/mL) was calculated from the maximally enhancing region within each tumor in the nephrogenic phase. Mean enhancement (ME) and mean iodine concentration (MIC) were stratified by histology, and across FNG for clear cell renal cell carcinoma (ccRCC) and compared between groups using Student’s t-test. RESULTS: Our study cohort consisted of 39 separate renal solid enhancing masses. Diagnosis was ccRCC in 22 (56.4%), non-ccRCC in 12 (30.8%), and oncocytoma in 5 (12.8%). Among RCCs, both ME and MIC were statistically significantly higher for ccRCCs versus non-ccRCCs (ME: 116.0 vs. 65.9 HU, p=0.02; MIC: 5.5 vs 3.8 mg/mL, p=0.02).Among ccRCCs, ME was lower for high grade (FNG 3 and 4) versus low grade (FNG 1 and 2) tumors (86.2 vs. 145.9 HU, p=0.1), while MIC was higher (5.7 vs 5.4 mg/mL, p=0.7), however these relationships did not reach statistical significance. Further, ME was higher for chromophobe RCC versus oncocytoma (115 vs. 104.4 HU), while MIC was lower (5.3 vs 6.7 mg/mL, no statistical analysis performed due to small sample size). CONCLUSIONS: DECT imaging with tumor MIC may improve the diagnostic ability to differentiate between renal mass histologies compared to standard contrast enhanced CT imaging. Our preliminary data shows a trend towards higher iodine concentration in high grade ccRCCs compared to low grade tumors and benign histologies. Further investigation with larger cohorts is needed to delineate diagnostic thresholds and allow correlation with oncologic outcomes that could lead to improved prognostic tools. Source of Funding: None © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e1083-e1083 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Rishi Sekar More articles by this author Giuseppe Toia More articles by this author Achille Mileto More articles by this author George Schade More articles by this author Expand All Advertisement Loading ...

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.

Assessment of the extracellular volume fraction for the grading of clear cell renal cell carcinoma: first results and histopathological findings.

To assess the potential of T1 mapping-based extracellular volume fraction (ECV) for the identification of higher grade clear cell renal cell carcinoma (cRCC), based on histopathology as the reference standard. For this single-center, institutional review board-approved prospective study, 27 patients (17 men, median age 62 ± 12.4years) with pathologic diagnosis of cRCC (nucleolar International Society of Urological Pathology (ISUP) grading) received abdominal MRI scans at 1.5T using a modified Look-Locker inversion recovery (MOLLI) sequence between January 2017 and June 2018. Quantitative T1 values were measured at different time points (pre- and postcontrast agent administration) and quantification of the ECV was performed on MRI and histological sections (H&E staining). Reduction in T1 value after contrast agent administration and MR-derived ECV were reliable predictors for differentiating higher from lower grade cRCC. Postcontrast T1diff values (T1diff = T1 difference between the native and nephrogenic phase) and MR-derived ECV were significantly higher for higher grade cRCC (ISUP grades 3-4) compared with lower grade cRCC (ISUP grades 1-2) (p < 0.001). A cutoff value of 700ms could distinguish higher grade from lower grade tumors with 100% (95% CI 0.69-1.00) sensitivity and 82% (95% CI 0.57-0.96) specificity. There was a positive and strong correlation between MR-derived ECV and histological ECV (p < 0.01, r= 0.88). Interobserver agreement for quantitative longitudinal relaxation times in the T1 maps was excellent. T1 mapping with ECV measurement could represent a novel in vivo biomarker for the classification of cRCC regarding their nucleolar grade, providing incremental diagnostic value as a quantitative MR marker. • Reduction in MRI T1 relaxation times after contrast agent administration and MR-derived extracellular volume fraction are useful parameters for grading of clear cell renal cell carcinoma (cRCC). • T1 differences between the native and the nephrogenic phase are higher for higher grade cRCC compared with lower grade cRCC and MRI-derived extracellular volume fraction (ECV) and histological ECV show a strong correlation. • T1 mapping with ECV measurement may be helpful for the noninvasive assessment of cRCC pathology, being a safe and feasible method, and it has potential to optimize individualized treatment options, e.g., in the decision of active surveillance.

Impact of noise-optimized virtual monoenergetic dual-energy computed tomography on image quality in patients with renal cell carcinoma

ObjectiveThe aim of this study was to evaluate the impact of a noise-optimized virtual monoenergetic imaging (VMI+) reconstruction technique on image quality and lesion delineation in patients with renal cell carcinoma (RCC) undergoing abdominal dual-energy computed tomography (DECT). Materials and methodsFifty-two patients (33 men; 61.5±13.6years) with RCC underwent contrast-enhanced DECT during the corticomedullary and nephrogenic phase of renal enhancement. DECT datasets were reconstructed with standard linearly-blended (M_0.6), as well as traditional virtual monoenergetic (VMI) and VMI+ algorithms in 10-keV increments from 40 to 100 keV. Contrast-to-noise (CNR) and tumor-to-cortex ratios for corticomedullary- and nephrogenic-phase images were objectively measured by a radiologist with 3 years of experience. Subjective image quality and RCC delineation were evaluated by three independent radiologists. ResultsGreatest CNR values were found for 40-keV VMI+ series in both corticomedullary- (8.9±4.9) and nephrogenic-phase (7.1±4.6) images and were significantly higher compared to all other reconstructions (P<0.001). Furthermore, tumor-to-cortex ratios were highest for 40-keV nephrogenic-phase VMI+ (2.1±3.5; P≤0.016), followed by 50-keV and 60-keV VMI+ (2.0±3.2 and 1.8±2.8, respectively). Qualitative image quality scored highest for 50-keV VMI+ series in corticomedullary-phase reconstructions and 60-keV in nephrogenic-phase reconstructions (P≤0.031). Highest scores for lesion delineation were assigned for 40-keV VMI+ reconstructions (P≤0.074). ConclusionLow-keV VMI+ reconstructions lead to improved image quality and lesion delineation of corticomedullary- and nephrogenic-phase DECT datasets in patients with RCC.

Correlation of computed tomography characteristics of cystic renal cell carcinoma with histopathology

Abstract Background Renal cysts are common incidental findings in computed tomography (CT) of the abdomen and range from benign to cystic renal cell carcinoma (RCC). Cystic RCC has various pathology, clinical prognosis, and treatment options. Objectives To compare preoperative CT of cystic RCC with histopathology. Methods Preoperative CT of histopathologically proven cystic RCCs were retrospectively reviewed in this single-center cross-sectional observational study. Two investigators recorded consensus CT features for each cystic RCC. The means of descriptive continuous data were calculated. A chi-square test, Fisher’s exact test, or an ANOVA were used for compare the frequency of findings for each histopathological subtype. Results Of 38 cystic RCCs, 25 were clear cell RCC, 5 were papillary RCC, 4 were multilocular cystic RCC, 1 was chromophobe RCC, and 3 were mixed type/other type (papillary/chromophobe, clear cell/chromophobe, and sarcomatoid type). We classified 36 lesions as Bosniak category IV and 2 lesions as Bosniak category III. There was no significant difference in cyst attenuation in any phase. Solid attenuation of the tumors was significantly different for each type in corticomedullary and nephrogenic phases (P = 0.001 and 0.042, respectively). Clear cell RCC was enhanced the most on corticomedullary and nephrogenic phases (means 135.5 and 112.1 Hounsfield Units, respectively). Septal thickening, enhancement, and multilocularity were significantly different between subtypes, particularly in multilocular cystic RCCs (P = 0.018, 0.018, and 0.02, respectively). Conclusion Preoperative CT findings may help clinicians and radiologists to predict tumor subtypes and aid treatment planning.

Commentary on “Predicting the histology of small renal masses using preoperative dynamic contrast-enhanced magnetic resonance imaging.” Kim JH, BaeJH, Lee KW, Kim ME, Park SJ, Park JY, Department of Urology, Soonchunhyang University Hospital, Seoul, Korea

To study if the magnetic resonance imaging can predict the histologic type of small renal cell carcinoma. Dynamic contrast-enhanced magnetic resonance imaging was performed on 63 patients with computed tomography-suspected or ultrasonography-suspected small (≤ 4 cm) renal cell carcinoma from February 2008 to February 2010. Percentage signal intensity change, tumor-to-cortex enhancement index during precontrast phase, corticomedullary phase, and nephrogenic phase were investigated. Among the 60 patients, 42 were proven to have clear cell renal cell carcinoma and 18 patients were proven to have nonclear cell renal cell carcinoma (10 patients with papillary renal cell carcinoma and 8 patients with chromophoberenal cell carcinoma). The percentage signal intensity change in the clear cell-type was higher only in the corticomedullary phase (P = 0.002). The tumor-to-cortex enhancement index in the clear cell type was higher in the corticomedullary and nephrogenic phases (P = 0.007 and P = 0.041, respectively). The most valuable marker was percentage signal intensity change in the corticomedullary phase (area under the receiver operating characteristic curve 0.85). The cutoff value of percentage signal intensity change in the corticomedullary phase was 173%, and the sensitivity and specificity were 81% and 87.5%, respectively. Dynamic contrast-enhanced magnetic resonance imaging could be useful for discriminating the clear cell type from nonclear cell type in small renal cell carcinoma with high sensitivity and specificity.

Predicting the Histology of Small Renal Masses Using Preoperative Dynamic Contrast-enhanced Magnetic Resonance Imaging

To study whether magnetic resonance imaging can predict the histologic type of small renal cell carcinoma. Dynamic contrast-enhanced magnetic resonance imaging was performed in 63 patients with computed tomography- or ultrasonography-suspected small (≤ 4 cm) renal cell carcinoma from February 2008 to February 2010. Percentage signal intensity change, tumor-to-cortex enhancement index during precontrast phase, corticomedullary phase, and nephrogenic phase were investigated. Among the 60 patients, 42 were proven to have clear cell renal cell carcinoma and 18 patients were proven to have non-clear cell renal cell carcinoma (10 patients with papillary renal cell carcinoma, 8 patients with chromophobe renal cell carcinoma). The percentage signal intensity change in the clear cell type was higher only in the corticomedullary phase (P = .002). The tumor-to-cortex enhancement index in the clear cell type was higher in the corticomedullary and nephrogenic phases (P = .007 and P = .041, respectively). The most valuable marker was percentage signal intensity change in the corticomedullary phase (area under the receiver operating characteristic curve 0.85). The cut-off value of percentage signal intensity change in the corticomedullary phase was 173%, and the sensitivity and specificity were 81% and 87.5%, respectively. Dynamic contrast-enhanced magnetic resonance imaging could be useful for discriminating the clear cell type from non-clear cell type in small renal cell carcinoma with high sensitivity and specificity.

Preoperative imaging in renal cell cancer

Renal cell cancer (RCC) represents the fifth most common cancer in men, with a rising incidence. Radical cancer surgery remains the only curative treatment in localized and advanced RCC. Therefore, preoperative imaging is most important for the planning of the surgical approach and strategy. The aim of any preoperative imaging in RCC is to differentiate benign from malignant lesions, to adequately assess tumor size, localization and organ confinement, to identify lymph node and/or visceral metastases, and to reliably predict the presence and extent of any thrombus of the vena cava. It is our aim to review the current status of preoperative imaging modalities in RCC. Computed tomography (CT) remains the most appropriate imaging modality to differentiate benign from malignant lesions. Although RCC can appear as iso-, hyper- or hypodense lesions on native CT scans, it usually demonstrates a significant contrast enhancement of about 115 HU and intratumoral areas of necrosis following the intravenous application of contrast medium. Benign masses such as renal oncocytoma are most often homogenous lesions exhibiting hypodensity compared to the normal renal parenchyma following the i.v. application of contrast dye. CT accurately predicts the tumor size with only a 0.5 cm difference as compared to the pathological size of the lesion. The identification of lymph node metastases still remains a problem since the limiting size is 4 mm and CT will result in a false negative rate of about 10%, especially in the presence of micrometastases; the false positive rate of 3-43% is mainly due to reactive hyperplasia. New technologies, such as the multidetector CT with thin collimation and multiplanar reformatting, might result in a diagnostic improvement. The involvement of the adrenal gland can be accurately predicted by CT scans or MRI, allowing an adrenal sparing approach in the case of unsuspicious findings. The detection of visceral metastases appears to be crucial since it has been shown that even patients with metastatic disease might benefit from radical nephrectomy followed by systemic immunotherapy in the case of a good performance status, and the presence of lymph node and pulmonary metastases only. Involvement of the renal vein and the vena cava with tumor thrombus formation will change the surgical strategy. Preoperatively, the presence and the cranial extent of the thrombus need to be known in order to plan the surgical approach. With regard to the extent of renal vein thrombi, a three phase helical CT scan is most appropriate; for vena caval thrombi only a MRI examination is able to accurately identify any infra- or suprahepatic as well as intracardial extension of the thrombus. The identification of multifocal lesions remains another unsolved problem in preoperative imaging techniques for RCC. Compared to the pathohistological analysis of nephrectomy specimens, neither ultrasonography, color duplex sonography nor regular CT scans are able to identify multifocal lesions with acceptable sensitivity and specificity. The evaluation of unenhanced CT scans together with the enhanced corticomedullary and the nephrogenic phase result in a 100% sensitivity and might represent a valuable option. Angiography has basically been abandoned from the armory of routine imaging techniques. It has, however, a current role in terms of the embolization of large tumors to reduce intraoperative blood loss, and in the palliative management of pain and bleeding due to RCC not amenable to surgery. Finally, we present a diagnostic algorithm for the most informative imaging techniques in the evaluation of RCC.