Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.

BACKGROUND: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics. METHODS: This retrospective study included 122 patients diagnosed as ccRCC after surgical resection. They were divided into a training set (n = 86) and a testing set(n = 36). CEUS radiographic features were extracted from CEUS images, and XGBoost ML models (US, CP, and MP model) with independent features at different phases were established. Multivariate regression analysis was performed on the characteristics of different radiomics phases to determine the indicators used for developing the prediction model of the combined CEUS model and establishing the XGBoost model. The training set was used to train the above four kinds of radiomics models, which were then tested in the testing set. Radiologists evaluated tumor characteristics, established a CEUS reading model, and compared the diagnostic efficacy of CEUS reading model with independent characteristics and combined CEUS model prediction models. RESULTS: The combined CEUS radiomics model demonstrated the best performance in the training set, with an area under the curve (AUC) of 0.84, accuracy of 0.779, sensitivity of 0.717, specificity of 0.879, positive predictive value (PPV) of 0.905, and negative predictive value (NPV) of0.659. In the testing set, the AUC was 0.811, with an accuracy of 0.784, sensitivity of 0.783, specificity of 0.786, PPV of 0.857, and NPV of 0.688. CONCLUSIONS: The radiomics model based on CEUS exhibits high accuracy in non-invasive prediction of ccRCC. This model can be utilized for non-invasive detection of WHO/ISUP nuclear grading of ccRCC and can serve as an effective tool to assist clinical decision-making processes.

Multiphase comparative study for WHO/ISUP nuclear grading diagnostic model based on enhanced CT images of clear cell renal cell carcinoma.

To compare and analyze the diagnostic value of different enhancement stages in distinguishing low and high nuclear grade clear cell renal cell carcinoma (ccRCC) based on enhanced computed tomography (CT) images by building machine learning classifiers. A total of 51 patients (Dateset1, including 41 low-grade and 10 high-grade) and 27 patients (Independent Dateset2, including 16 low-grade and 11 high-grade) with pathologically proven ccRCC were enrolled in this retrospective study. Radiomic features were extracted from the corticomedullary phase (CMP), nephrographic phase (NP), and excretory phase (EP) CT images, and selected using the recursive feature elimination cross-validation (RFECV) algorithm, the group differences were assessed using T-test and Mann-Whitney U test for continuous variables. The support vector machine (SVM), random forest (RF), XGBoost (XGB), VGG11, ResNet18, and GoogLeNet classifiers are established to distinguish low-grade and high-grade ccRCC. The classifiers based on CT images of NP (Dateset1, RF: AUC = 0.82 ± 0.05, ResNet18: AUC = 0.81 ± 0.02; Dateset2, XGB: AUC = 0.95 ± 0.02, ResNet18: AUC = 0.87 ± 0.07) obtained the best performance and robustness in distinguishing low-grade and high-grade ccRCC, while the EP-based classifier performance in poorer results. The CT images of enhanced phase NP had the best performance in diagnosing low and high nuclear grade ccRCC. Firstorder_Kurtosis and firstorder_90Percentile feature play a vital role in the classification task.

Three-dimensional reconstruction of renal tumor anatomy for preoperative planning of robotic partial nephrectomy in renal cell carcinoma cases with duplex kidney: a case report.

BACKGROUND: The duplex kidney is one of the common congenital anomalies of the kidney and urinary tract. We present two cases of renal tumor accompanied with ipsilateral duplex kidney. The image of the tumor, renal artery system and collecting system were rendered by AI software (Fujifilm's Synapse® AI Platform) to support the diagnosis and surgical planning. CASE PRESENTATION: Two Vietnamese patients (a 45-year-old man and a 54-year-old woman) with incidental cT1 renal cell carcinoma (RCC) were confirmed to have ipsilateral duplex kidneys by 3D reconstruction AI technique. One patient had a Renal score 9ah tumor of left kidney while the other had a Renal score 9 × tumor of right kidney in which a preoperative CT scan failed to identify a diagnosis of duplex kidney. Using the Da Vinci platform, we successfully performed robotic partial nephrectomy without any damage to the collecting system in both cases. CONCLUSION: RCC with duplex kidneys is a rare condition. By utilizing a novel AI reconstruction technique with adequate information, two patients with RCC in duplex kidneys were successfully performed robotic partial nephrectomy without complication.

Utility of Chemical Shift Imaging and Diffusion-weighted Images/Apparent Diffusion Coefficient Maps as a Tool for Evaluation of Solid Renal Tumors.

OBJECTIVES: To study the utility of chemical shift imaging (CSI) and diffusion-weighted images (DWI)/apparent diffusion coefficient (ADC) maps for the evaluation of solid renal tumors. METHODS: Magnetic resonance imaging (MRI) has an equivalent application as computerized tomography (CT) in the characterization of renal masses. It offers a radiation-free imaging technique and has a better soft tissue contrast than CT. Also, MRI is favored in patients with chronic kidney disease. MRI is useful when findings on CT are equivocal. The role of DWI in characterizing solid renal lesions as malignant is encouraging, and DWI can be particularly useful when gadolinium is contraindicated. CSI is useful in differentiating angiomyolipoma (AML) from clear cell (cc) renal cell carcinoma (RCC). We did a cross-sectional study on 24 patients with solid renal masses. MRI of the upper abdomen (from the dome of the diaphragm to the iliac crest) will be done on an MRI machine in our department (1.5T, ACHIEVA, Phillips medical system) using the torso coil. RESULT: There was no significant association seen in terms of ADC values and histological subtypes (χ2 = 11.222, p = 0.082). In our study, 50% (one out of two) of AML showed a signal drop, whereas 40% of cases (6 out of 15) of ccRCC and 66% (two out of three) of papillary RCC showed a signal drop. CONCLUSION: In this article, we concluded CSI, although a useful tool to look for microscopic fat, can't be used as a reliable marker to rule in cc-carcinoma as both AML and papillary cell carcinoma have microscopic fat. Further, no histological classification can be done on the basis of DWI/ADC images.

High-frame-rate contrast-enhanced ultrasound to differentiate between clear cell renal cell carcinoma and angiomyolipoma.

BACKGROUND: To investigate the diagnostic efficacy of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in differentiating between clear cell renal cell carcinoma (CCRCC) and angiomyolipoma (AML). METHODS: A retrospective study was performed on the clinical data of 79 patients diagnosed with CCRCC and 31 patients diagnosed with AML at the First Affiliated Hospital of Nanchang University between October 2022 and December 2023. Conventional ultrasound (US) and H-CEUS examinations were conducted on all patients prior to surgery, dynamic images were recorded from the US, and the qualitative and quantitative parameters of H-CEUS were collected. The t-test, χ² test and non-parametric Mann-Whitney test were employed to assess differences in clinical data, US characteristics, and qualitative and quantitative parameters of H-CEUS between the CCRCC and AML groups. The independent risk factors of CCRCC were identified using binary logistic regression. The receiver operator characteristic (ROC) curve was constructed to evaluate the diagnostic effectiveness of clinical + US and H-CEUS in differentiating between CCRCC and AML. RESULTS: The CCRCC group and the AML group exhibited significant differences in patient gender, operation mode, nodular echo, and nodule blood flow (χ²=11.698, -, -,=10.582; P<0.001, <0.001, <0.001, and = 0.014, respectively). In addition, the H-CEUS qualitative analysis demonstrated significant differences between the AML group and the CCRCC group with respect to enhancement mode, regression mode, peak intensity, enhancement uniformity, no enhancement, and presence or absence of pseudocapsule (χ²=41.614, -, -, = 2.758, = 42.099, -; P<0.001, <0.001, <0.001, 0.097, <0.001, and <0.001, respectively). The Arrival time (AT) in the CCRCC group was significantly shorter than that in the AML group, as determined by quantitative analysis of H-CEUS (Z=-3.266, P = 0.001). Furthermore, the Peak intensity (PI), Ascent slope (AS), and The area under the curve (AUC) exhibited significantly higher values in the CCRCC group compared to the AML group (Z=-2.043,=-2.545,=-3.565; P = 0.041, = 0.011, and <0.001, respectively). Logistic regression analysis indicated that only gender, nodule echo, the pseudocapsule, AS, and AUC of H-CEUS were independent risk factors of CCRCC. The ROC curve revealed that combining gender and nodule echo yielded a sensitivity of 92.4%, specificity of 64.5%, and an AUC of 0.847 in distinguishing between CCRCC and AML. When combining the H-CEUS parameters of pseudocapsule, AS, and AUC, the sensitivity, specificity, and AUC for distinguishing between CCRCC and AML were 84.8%, 96.8%, and 0.918, respectively. No statistically significant difference was observed in the diagnostic effectiveness of the two methods (Z=-1.286, P = 0.198). However, H-CEUS demonstrated better AUC and specificity. CONCLUSIONS: H-CEUS enhances the sensitivity and specificity of differentiating between CCRCC and AML by improving the temporal resolution, offering a more precise diagnostic foundation for identifying the most appropriate therapy for patients.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology.

BACKGROUND: The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model. METHODS: We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis. RESULTS: Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model. CONCLUSIONS: The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Diffuse Intense 18 F-PSMA-1007 Uptake of the Breasts in a Woman With TFE3-Rearranged Renal Cell Carcinoma.

ABSTRACT: A 23-year-old woman presenting with gross hematuria was found to have a left renal mass suspicious for renal cell carcinoma on abdominal contrast-enhanced CT. An 18 F-PSMA-1007 PET/CT scan was performed for evaluating the renal mass. 18 F-PSMA-1007 PET/CT showed focal activity of the renal mass, which was a transcription factor E3-rearranged renal cell carcinoma proved after nephrectomy. Surprisingly, diffuse heterogeneous intense activity of the bilateral breasts and moderate activity of the right accessory breast was observed. There was no morphological abnormality of the bilateral breasts and right accessory breast on CT images, indicating physiological PSMA uptake.

Detection of two synchronous histologically different renal cell carcinoma subtypes in the same kidney: a case report and review of the literature.

INTRODUCTION: Renal cell carcinoma (RCC) is the dominant primary renal malignant neoplasm, encompassing a significant portion of renal tumors. The presence of synchronous yet histologically distinct ipsilateral RCCs, however, is an exceptionally uncommon phenomenon that is rather under-described in the literature regarding etiology, diagnosis, management, and later outcomes during follow-up. CASE PRESENTATION: We aim to present the 9th case of a combination chromophobe RCC (ChRCC) and clear cell RCC (ccRCC) in literature, according to our knowledge, for a 69-year-old North African, Caucasian female patient who, after complaining of loin pain and hematuria, was found to have two right renal masses with preoperative computed tomography (CT) and underwent right radical nephrectomy. Pathological examination later revealed the two renal masses to be of different histologic subtypes. CONCLUSION: The coexistence of dissimilar RCC subtypes can contribute to diverse prognostic implications. Further research should focus on enhancing the complex, yet highly crucial, preoperative detection and pathological examination to differentiate multiple renal lesions. Planning optimal operative techniques (radical or partial nephrectomy), selecting suitable adjuvant regimens, and reporting long-term follow-up outcomes of patients in whom synchronous yet different RCC subtypes were detected are of utmost importance.

Development and validation of a clinical and ultrasound features-based nomogram for preoperative differentiation of renal urothelial carcinoma and central renal cell carcinoma.

PURPOSE: This study aimed to develop and validate an ultrasound (US)-based nomogram for the preoperative differentiation of renal urothelial carcinoma (rUC) from central renal cell carcinoma (c-RCC). METHODS: Clinical data and US images of 655 patients with 655 histologically confirmed malignant renal tumors (521 c-RCCs and 134 rUCs) were collected and divided into training (n = 455) and validation (n = 200) cohorts according to examination dates. Conventional US and contrast-enhanced US (CEUS) tumor features were analyzed to determine those that could discriminate rUC from c-RCC. Least absolute shrinkage and selection operator regression was applied to screen clinical and US features for the differentiation of rUC from c-RCC. Using multivariate logistic regression analysis, a diagnostic model of rUC was constructed and visualized as a nomogram. The diagnostic model's performance was assessed in the training and validation cohorts by calculating the area under the receiver operating characteristic curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness of the US-based nomogram. RESULTS: Seven features of both clinical features and ultrasound imaging were selected to build the diagnostic model. The nomogram achieved favorable discrimination in the training (AUC = 0.996, 95% CI: 0.993-0.999) and validation (AUC = 0.995, 95% CI: 0.974, 1.000) cohorts, and good calibration (Brier scores: 0.019 and 0.016, respectively). DCA demonstrated the clinical usefulness of the US-based nomogram. CONCLUSION: A noninvasive clinical and US-based nomogram combining conventional US and CEUS features possesses good predictive value for differentiating rUC from c-RCC.

FDG PET/CT in the Diagnosis and After Sunitinib Follow-up of Synchronous Base of Tongue and Thyroid Metastases From Renal Cell Carcinoma.

ABSTRACT: Renal cell carcinoma (RCC) is a leading cause of mortality among genitourinary malignancies with limited therapeutic options. The hematogenous route, lymphatic spread, and direct invasion have been documented in RCC. Usually, metastases are regional lymph nodes, lungs, bone, liver, adrenal glands, contralateral kidney, and brain. Metastases to the rare sites such as skin, breast, head and neck were documented in the literature. In the present case, we describe the synchronous metastases to the base of the tongue and thyroid gland in RCC and the response to sunitinib therapy on 18F-FDG PET/CT.

Ablative Radioembolization (SIRT) of a Large Inoperable Renal Cell Carcinoma Invading the Liver That Resulted in Hematuria Control and Complete Necrosis by mRECIST Criteria.

ABSTRACT: We report the successful application of radioembolization (SIRT) in a 77-year-old man with end-stage renal disease on hemodialysis and repeated episodes of macroscopic hematuria due to a large renal cell carcinoma of the right kidney extending to liver segment VI. A compassionate SIRT therapy was performed with resin microspheres through the upper pole renal artery and the feeding segmental artery of liver segment VI. Hematuria was resolved after treatment, and 4 months later, a follow-up CT scan revealed tumor size reduction and complete tumor necrosis (Response Evaluation Criteria in Solid Tumors criteria). Ablative SIRT therapy could be a safe and efficient option in a large inoperable RCC.

Percutaneous Microwave Ablation Versus Robot-Assisted Partial Nephrectomy for Stage I Renal Cell Carcinoma: A Propensity-Matched Cohort Study Focusing Upon Long-Term Follow-Up of Oncologic Outcomes.

PURPOSE: To retrospectively compare long-term oncologic outcomes of percutaneous computed tomography-guided microwave ablation (MWA) and robot-assisted partial nephrectomy (RAPN) for the treatment of stage 1 (T1a and T1b) renal cell carcinoma (RCC) patients. MATERIALS AND METHODS: Institutional database research identified all T1 RCC patients who underwent either MWA or RAPN. Models were adjusted with propensity score matching. Kaplan-Meier log-rank test analyses and Cox proportional hazard regression models were used to compare the oncologic outcomes. Patient and tumor characteristics, technical success as well as oncologic outcomes were evaluated and compared between the 2 groups. RESULTS: After propensity score matching, a total of 71 patients underwent percutaneous MWA (mean age 70 ± 10 years) and 71 underwent RAPN (mean age 60 ± 9 years). At 8-year follow-up, the estimated survival rates for MWA cohort were 98% (95% confidence interval [CI] 95-100%) for overall survival, 97% (95% CI 93-100%) for recurrence-free survival, and 97% (95% CI 93-100%) for metastasis-free survival. The matched cohort that underwent RAPN exhibited survival rates of 100% (95% CI 100-100%) for overall survival, 98% (95% CI 94-100%) for recurrence-free survival, and 98% (95% CI 94-100%) for metastasis-free survival. After performing log-rank testing, these rates were not significantly different (p values of 0.44, 0.67, and 0.67, respectively). CONCLUSION: The results of the present study suggest that both MWA and RAPN are equally effective in terms of oncologic outcome for the treatment of T1 RCC.

Comparison of conventional diffusion-weighted imaging and intravoxel incoherent motion in differentiating between chromophobe renal cell carcinoma and renal oncocytoma: a preliminary study.

OBJECTIVE: Quantitative comparison of the diagnostic efficacy of conventional diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in differentiating between chromophobe renal cell carcinoma (ChRCC) from renal oncocytoma (RO). METHODS: A total of 48 patients with renal tumours who had undergone DWI and IVIM were divided into two groups-ChRCC (n = 28) and RO (n = 20) groups, and the apparent diffusion coefficient (ADC), true diffusivity (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) and their diagnostic efficacy were compared between the two groups. RESULTS: The D* values were higher in the ChRCCs group compared to the RO groups (0.019 ± 0.003 mm2/s vs 0.008 ± 0.002 mm2/s, P < .05). Moreover, the ADC, D and f values were higher in ROs compared to ChRCCs (0.61 ± 0.08 × 10-3 mm2/s vs 0.51 ± 0.06 × 10-3 mm2/s, 1.02 ± 0.15 × 10-3 mm2/s vs 0.86 ± 0.07 × 10-3 mm2/s, 0.41 ± 0.05 vs 0.28 ± 0.02, P < .05). The areas of the ADC, D, D* and f values under the ROC curves in differentiating ChRCCs from ROs were 0.713, 0.839, 0.856 and 0.906, respectively. The cut-off values of ADC, D, D* and f were 0.54, 0.91, 0.013 and 0.31, respectively. The AUC, sensitivity, specificity and accuracy of the f values were 0.906, 89.3%, 80.0% and 89.6%, respectively. For pairwise comparisons of ROC curves and diagnostic efficacy, IVIM parameters, that is, D, D* and f offered better diagnostic accuracy than ADC in differentiating ChRCCs from ROs (P = .013, .016, and .008) with f having the highest diagnostic accuracy. CONCLUSION: IVIM parameters presented better performance than ADC in differentiating ChRCCs from ROs. ADVANCES IN KNOWLEDGE: (1) D* values of ChRCCs were higher, while ADC, D and f values were lower than those of RO tumours. (2) f values had the highest diagnostic efficacy in differentiating ChRCC from RO. (3) IVIM parameters, that is, D, D* and f offered better diagnostic accuracy than ADC in differentiating ChRCC from RO (P=.013, .016, and .008).

CT-based radiomics model using stability selection for predicting the World Health Organization/International Society of Urological Pathology grade of clear cell renal cell carcinoma.

OBJECTIVES: This study aimed to develop a model to predict World Health Organization/International Society of Urological Pathology (WHO/ISUP) low-grade or high-grade clear cell renal cell carcinoma (ccRCC) using 3D multiphase enhanced CT radiomics features (RFs). METHODS: CT data of 138 low-grade and 60 high-grade ccRCC cases were included. RFs were extracted from four CT phases: non-contrast phase (NCP), corticomedullary phase, nephrographic phase, and excretory phase (EP). Models were developed using various combinations of RFs and subjected to cross-validation. RESULTS: There were 107 RFs extracted from each phase of the CT images. The NCP-EP model had the best overall predictive value (AUC = 0.78), but did not significantly differ from that of the NCP model (AUC = 0.76). By considering the predictive ability of the model, the level of radiation exposure, and model simplicity, the overall best model was the Conventional image and clinical features (CICFs)-NCP model (AUC = 0.77; sensitivity 0.75, specificity 0.69, positive predictive value 0.85, negative predictive value 0.54, accuracy 0.73). The second-best model was the NCP model (AUC = 0.76). CONCLUSIONS: Combining clinical features with unenhanced CT images of the kidneys seems to be optimal for prediction of WHO/ISUP grade of ccRCC. This noninvasive method may assist in guiding more accurate treatment decisions for ccRCC. ADVANCES IN KNOWLEDGE: This study innovatively employed stability selection for RFs, enhancing model reliability. The CICFs-NCP model's simplicity and efficacy mark a significant advancement, offering a practical tool for clinical decision-making in ccRCC management.

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF. MATERIALS AND METHODS: Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included. Twenty-one and seventeen patients were set aside for the internal and external test sets, respectively. Pre-operative T1-weighted MRI acquired at 60 s following a contrast injection (T1w-60) were collected. For each T1w-60 data, two regions of interest (ROIs) were manually drawn: the perinephric fat tissue and an aorta segment on the same level as the targeted kidney. Preprocessing steps included resizing voxels, N4 Bias Correction filtering, and aorta-based normalization. For each patient, 851 radiomics features were extracted from the ROI of perinephric fat tissue. Gender and BMI were added as clinical factors. Least Absolute Shrinkage and Selection Operator was adopted for feature selection. We trained and evaluated five models using a 4-fold cross validation. The final model was chosen based on the highest mean AUC across four folds. The performance of the final model was evaluated on the internal and external test sets. RESULTS: A total of 15 features were selected in the final set. The final model achieved the accuracy, sensitivity, specificity, and AUC of 81% (95% confidence interval, 61.9-95.2%), 72.7% (42.9-100%), 90% (66.7-100%), and 0.855 (0.615-1.0), respectively on the internal test set, and 88.2% (70.6-100%), 100% (100-100%), 80% (50%-100%), 0.971 (0.871-1.0), respectively on the external test set. CONCLUSIONS: Our study demonstrated the feasibility of machine learning algorithms trained with MRI-based radiomics features for APF prediction. Further studies with a multi-center approach are necessary to validate our findings.

Can 18 F FDG PET/CT metabolic parameters be used to noninvasively differentiate between different histopathological subtypes and Fuhrman grades of renal cell cancer?

AIM: To evaluate relationship between metabolic PET metabolic parameters and size of the primary tumor, various histopathological subtypes of renal cell carcinoma (RCC) and Fuhrman grade of the tumors. MATERIAL AND METHODS: Retrospective analysis of 93 biopsy-proven RCC patients who underwent pretreatment flourine 18 flourodeoxyglucose PET/computed tomography ( 18 F FDG PET/CT) was performed. Quantitative PET parameters, size of the primary tumor, histopathological subtypes and Fuhrman grades of the tumor were extracted. We tried to assess if there was any significant difference in the metabolic patterns of various histopathological subtypes of RCCs, Fuhrman grade of the tumors and size of the primary tumor. RESULTS: A significant correlation was noted between the size of primary tumor and maximum standardized uptake value (SUV max ), metabolic tumor volume (MTV) and total lesion glycolysis (TLG) ( P  < 0.01, P  < 0.001 and P  < 0.001, respectively). SUV max values correlated significantly with the histopathological subtype ( P  < 0.001). Further sub-analyses was also done by segregating the patients into Low grade (Fuhrman grade 1 and 2) vs. High grade (Fuhrman grade 3 and 4). SUV max , MTV and TLG were significantly different between high grade vs. low grade tumors. ROC analysis yielded cut off values for SUV max , MTV and TLG to differentiate between high grade from low grade tumors. CONCLUSION: FDG PET/CT with the use of metabolic PET parameters can differentiate between different histopathological subtypes of RCC. Incorporation of metabolic parameters into clinical practice can potentially noninvasively identify patients with low-grade vs. high-grade RCC.

Juxtaglomerular Cell Tumor Mimicking Renal Cell Carcinoma on 99m Tc-MIBI SPECT/CT.

ABSTRACT: Juxtaglomerular cell tumor or reninoma is an extremely rare, typically benign, renin-secreting tumor of the kidney that causes secondary hypertension. We describe 99m Tc-MIBI SPECT/CT findings in a case of juxtaglomerular cell tumor. The renal tumor showed isodensity and photopenia on 99m Tc-MIBI SPECT/CT. This case indicates that juxtaglomerular cell tumor can appear cold on 99m Tc-MIBI SPECT/CT, mimicking renal cell carcinoma.