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.

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.

Percutaneous microwave ablation of T1a renal cell carcinomas: A 10-year single-center retrospective review.

INTRODUCTION: Partial nephrectomy is currently the preferred treatment option for T1a renal cell carcinomas (RCC), with nephron-sparing techniques, including microwave ablation, becoming more common in select patients. Primary aims are to document outcomes of microwave ablation for T1a RCCs in an Australian tertiary centre to add to the evidence for its safety and efficacy. METHODS: The prospectively maintained Sir Charles Gairdner Hospital Interventional Radiology database was retrospectively searched for all Renal Microwave ablations completed between June 2012 and February 2022. This database and a combination of hospital programmes including Agfa Impax PACS, Bossnet digital medical record and iSoft Clinical Manager were used to extract the relevant data which was anonymized and entered into an Excel spreadsheet for analysis. RESULTS: Forty-eight patients underwent microwave ablation for 50 tumours. Of these, there were two local and two distant recurrences. A fifth patient had metastases on presentation. Higher local recurrence rates were associated with larger tumour size (P = 0.043). Tumour proximity to the collecting system <4 mm was associated with higher rates of complications (P = 0.020). RENAL scores did not show statistically significant correlation with complications (P = 0.092) or local or distant recurrence. Notably, the study follow-up time was longer than many comparative studies (mean = 2796, ~7.66 years censoring for death and mean = 832 days, ~2.28 years not censoring for death). CONCLUSION: Consistent with the literature, this study further demonstrates that microwave ablation is a safe and efficacious option for treatment of T1a RCC.

Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma.

PURPOSE: To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions. RESULTS: For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set. CONCLUSION: The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.

The Prevalence and Radiologic Features of Renal Cancers Associated with FLCN, BAP1, SDH, and MET Germline Mutations.

Purpose To investigate the prevalence of FLCN, BAP1, SDH, and MET mutations in an oncologic cohort and determine the prevalence, clinical features, and imaging features of renal cell carcinoma (RCC) associated with these mutations. Secondarily, to determine the prevalence of encountered benign renal lesions. Materials and Methods From 25 220 patients with cancer who prospectively underwent germline analysis with a panel of more than 70 cancer-predisposing genes from 2015 to 2021, patients with FLCN, BAP1, SDH, or MET mutations were retrospectively identified. Clinical records were reviewed for patient age, sex, race/ethnicity, and renal cancer diagnosis. If RCC was present, baseline CT and MRI examinations were independently assessed by two radiologists. Summary statistics were used to summarize continuous and categorical variables by mutation. Results A total of 79 of 25 220 (0.31%) patients had a germline mutation: FLCN, 17 of 25 220 (0.07%); BAP1, 22 of 25 220 (0.09%); SDH, 39 of 25 220 (0.15%); and MET, one of 25 220 (0.004%). Of these 79 patients, 18 (23%) were diagnosed with RCC (FLCN, four of 17 [24%]; BAP1, four of 22 [18%]; SDH, nine of 39 [23%]; MET, one of one [100%]). Most hereditary RCCs demonstrated ill-defined margins, central nonenhancing area (cystic or necrotic), heterogeneous enhancement, and various other CT and MR radiologic features, overlapping with the radiologic appearance of nonhereditary RCCs. The prevalence of other benign solid renal lesions (other than complex cysts) in patients was up to 11%. Conclusion FLCN, BAP1, SDH, and MET mutations were present in less than 1% of this oncologic cohort. Within the study sample size limits, imaging findings for hereditary RCC overlapped with those of nonhereditary RCC, and the prevalence of other associated benign solid renal lesions (other than complex cysts) was up to 11%. Keywords: Familial Renal Cell Carcinoma, Birt-Hogg-Dubé Syndrome, Carcinoma, Renal Cell, Paragangliomas, Urinary, Kidney © RSNA, 2024.

Renal cell carcinoma with an "uncoiling" tumor thrombus: intraoperative shift from level III to level IV.

BACKGROUND: The gold standard treatment for renal cell carcinoma (RCC) with tumor thrombus (TT) is complete surgical excision. The surgery is complex and challenging to the surgeon, especially with large tumor thrombus extending into the inferior vena cava (IVC) and right atrium. Traditionally, these difficult cases required the use of cardiopulmonary bypass (CPB) with or without deep hypothermic cardiac arrest, but in recent years, different surgical techniques derived from the field of liver transplantation have been used in efforts to avoid CPB. CASE PRESENTATION: We present a case of RCC with TT level IIIc (extending above major hepatic veins) that "uncoiled" intraoperatively into the right atrium after division of the IVC ligament, transforming into a level IV TT. Despite the new TT extension, the surgery was successfully completed exclusively through an abdominal approach without CPB and while using intraoperative transesophageal echocardiography (TEE) monitoring and a cardiothoracic team standby. CONCLUSIONS: This case highlights the need for a multidisciplinary approach and the utility of intraoperative continous TEE monitoring which helped to visualize the change of the TT venous extension, allowing the surgical teamto modify their surgical approach as needed avoiding a catastrophic event.

Percutaneous Image-Guided Cryoablation of Endophytic Renal Cell Carcinoma.

PURPOSE: Endophytic renal cancer treatment is a challenge. Due to difficulties in endophytic tumor visualization during surgical extirpation, image-guided percutaneous cryoablation (PCA) is an attractive alternative. The minimally invasive nature of PCA makes it favorable for comorbid patients as well as patients in which surgery is contraindicated. Oncological outcomes and complications after PCA of endophytic biopsy-proven renal cell carcinoma (RCC) were reviewed in this study. MATERIALS AND METHODS: Patients were included after a multidisciplinary team conference from January 2015 to November 2021. Inclusion criteria were endophytic biopsy-proven T1 RCC treated with PCA with one year of follow-up. Complications were reported according to the Cardiovascular and Interventional Radiological Society of Europe (CIRSE) classification system and the Clavien-Dindo classification (CDC) system. Major complications were defined as a grade ≥ 3 according to the CDC. RESULTS: Fifty-six patients were included with a total of 56 endophytic tumors treated during 61 PCA sessions. The median RENAL nephrometry score was 9 (IQR 2), and the mean tumor size was 25.7 mm (SD ± 8.9 mm). Mean hospitalization time was 0.39 (SD ± 1.1) days. At a mean follow-up of 996 days (SD ± 559), 86% of tumors were recurrence free after one PCA. No patients progressed to metastatic disease. According to the CIRSE classification, 10.7% (n = 6) had grade 3 complications, and 5.4% (n = 3) had CDC major complications. CONCLUSION: This study demonstrates that PCA of endophytic biopsy-proven T1 RCC is safe with few major complications and excellent local tumor control rates at almost three-year mean follow-up. LEVEL OF EVIDENCE 3: Retrospective cohort study.

Study of radiomics based on dual-energy CT for nuclear grading and T-staging in renal clear cell carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) is the most lethal subtype of renal cell carcinoma with a high invasive potential. Radiomics has attracted much attention in predicting the preoperative T-staging and nuclear grade of ccRCC. OBJECTIVE: The objective was to evaluate the efficacy of dual-energy computed tomography (DECT) radiomics in predicting ccRCC grade and T-stage while optimizing the models. METHODS: 200 ccRCC patients underwent preoperative DECT scanning and were randomized into training and validation cohorts. Radiomics models based on 70 KeV, 100 KeV, 150 KeV, iodine-based material decomposition images (IMDI), virtual noncontrasted images (VNC), mixed energy images (MEI) and MEI + IMDI were established for grading and T-staging. Receiver operating characteristic analysis and decision curve analysis (DCA) were performed. The area under the curve (AUC) values were compared using Delong test. RESULTS: For grading, the AUC values of these models ranged from 0.64 to 0.97 during training and from 0.54 to 0.72 during validation. In the validation cohort, the performance of MEI + IMDI model was optimal, with an AUC of 0.72, sensitivity of 0.71, and specificity of 0.70. The AUC value for the 70 KeV model was higher than those for the 100 KeV, 150 KeV, and MEI models. For T-staging, these models achieved AUC values of 0.83 to 1.00 in training and 0.59 to 0.82 in validation. The validation cohort demonstrated AUCs of 0.82 and 0.70, sensitivities of 0.71 and 0.71, and specificities of 0.80 and 0.60 for the MEI + IMDI and IMDI models, respectively. In terms of grading and T-staging, the MEI + IMDI model had the highest AUC in validation, with IMDI coming in second. There were statistically significant differences between the MEI + IMDI model and the 70 KeV, 100 KeV, 150 KeV, MEI, and VNC models in terms of grading (P < .05) and staging (P ≤ .001). DCA showed that both MEI + IDMI and IDMI models outperformed other models in predicting grade and stage of ccRCC. CONCLUSIONS: DECT radiomics models were helpful in grading and T-staging of ccRCC. The combined model of MEI + IMDI achieved favorable results.

The Association of Body Composition Parameters Measured by Computed Tomography with Cancer Stage, Prognosis, and Survival in Patients with Renal Cell Carcinoma.

OBJECTIVE: This study aims to investigate the association of preoperative body composition parameters, measured by computed tomography in patients undergoing surgery for renal cell carcinoma, with its stage and to survey the relationship with postoperative hospitalization duration and survival. METHODS: Demographic data, pathology results, cancer stages, and hospitalization duration of 104 patients undergoing surgery at the urology clinic due to renal cell carcinoma between 2019 and 2023 were analyzed retrospectively. On computed tomography scans acquired during diagnosis, visceral adipose tissue, subcutaneous adipose tissue, total adipose tissue, and skeletal muscle area were measured. The ratios of body composition parameters were computed. RESULTS: When the correlation between survival time and body composition in deceased patients was analysed, a moderate but significant correlation was observed between skeletal muscle area value and total adipose tissue / skeletal muscle area ratio (r=0.630, p=0.001; r=0.598, p=0.002). A significant and strong correlation was observed between total adipose tissue value and survival (r=0.704, p<0.001). Subcutaneous adipose tissue / skeletal muscle area was found to be an independent risk factor associated with mortality, and a ratio of 0.98 or less increased the mortality risk approximately 16-fold. CONCLUSION: The relationship between body composition parameters measured by computed tomography, which can be easily evaluated pre-treatment, and mortality, postoperative recovery and length of hospital stay can be evaluated, giving clinicians an idea about the potential difficulties that patients may encounter during the treatment process. For this purpose, the subcutaneous adipose tissue / skeletal muscle area ratio is the most helpful parameter that can be used.

Nomogram combining pre-operative clinical characteristics and spectral CT parameters for predicting the WHO/ISUP pathological grading in clear cell renal cell carcinoma.

PURPOSE: To develop a novel clinical-spectral-computed tomography (CT) nomogram incorporating clinical characteristics and spectral CT parameters for the preoperative prediction of the WHO/ISUP pathological grade in clear cell renal cell carcinoma (ccRCC). METHODS: Seventy-three ccRCC patients who underwent spectral CT were included in this retrospective analysis from December 2020 to June 2023. The subjects were pathologically divided into low- and high-grade groups (WHO/ISUP 1/2, n = 52 and WHO/ISUP 3/4, n = 21, respectively). Information on clinical characteristics, conventional CT imaging features, and spectral CT parameters was collected. Multivariate logistic regression analyses were conducted to create a nomogram combing clinical data and image data for preoperatively predicting the pathological grade of ccRCC, and the area under the curve (AUC) was utilized to assess the predictive performance of the model. RESULTS: Multivariate logistic regression analyses revealed that age, systemic immune-inflammation index (SII), and the slope of the spectrum curve in the cortex phase (CP-K) were independent predictors for predicting high-grade ccRCC. The clinical-spectral-CT model exhibited high evaluation efficacy, with an AUC of 0.933 (95% confidence interval [CI]: 0.878-0.998; sensitivity: 0.810; specificity: 0.923). The calibration curve revealed that the predicted probability of the clinical-spectral-CT nomogram could better fit the actual probability, with high calibration. The Hosmer-Lemeshow test showed that the model had a good fitness (χ2 = 5.574, p = 0.695). CONCLUSION: The clinical-spectral-CT nomogram has the potential to predict WHO/ISUP grading of ccRCC preoperatively.

Kidney scoring surveillance: predictive machine learning models for clear cell renal cell carcinoma growth using MRI.

INTRODUCTION: Classification of clear cell renal cell carcinoma (ccRCC) growth rates in patients with Von Hippel-Lindau (VHL) syndrome has several ramifications for tumor monitoring and surgical planning. Using two separate machine-learning algorithms, we sought to produce models to predict ccRCC growth rate classes based on qualitative MRI-derived characteristics. MATERIAL AND METHODS: We used a prospectively maintained database of patients with VHL who underwent surgical resection for ccRCC between January 2015 and June 2022. We employed a threshold growth rate of 0.5 cm per year to categorize ccRCC tumors into two distinct groups-'slow-growing' and 'fast-growing'. Utilizing a questionnaire of qualitative imaging features, two radiologists assessed each lesion on different MRI sequences. Two machine-learning models, a stacked ensemble technique and a decision tree algorithm, were used to predict the tumor growth rate classes. Positive predictive value (PPV), sensitivity, and F1-score were used to evaluate the performance of the models. RESULTS: This study comprises 55 patients with VHL with 128 ccRCC tumors. Patients' median age was 48 years, and 28 patients were males. Each patient had an average of two tumors, with a median size of 2.1 cm and a median growth rate of 0.35 cm/year. The overall performance of the stacked and DT model had 0.77 ± 0.05 and 0.71 ± 0.06 accuracies, respectively. The best stacked model achieved a PPV of 0.92, a sensitivity of 0.91, and an F1-score of 0.90. CONCLUSION: This study provides valuable insight into the potential of machine-learning analysis for the determination of renal tumor growth rate in patients with VHL. This finding could be utilized as an assistive tool for the individualized screening and follow-up of this population.

Deep learning algorithm (YOLOv7) for automated renal mass detection on contrast-enhanced MRI: a 2D and 2.5D evaluation of results.

INTRODUCTION: Accurate diagnosis and treatment of kidney tumors greatly benefit from automated solutions for detection and classification on MRI. In this study, we explore the application of a deep learning algorithm, YOLOv7, for detecting kidney tumors on contrast-enhanced MRI. MATERIAL AND METHODS: We assessed the performance of YOLOv7 tumor detection on excretory phase MRIs in a large institutional cohort of patients with RCC. Tumors were segmented on MRI using ITK-SNAP and converted to bounding boxes. The cohort was randomly divided into ten benchmarks for training and testing the YOLOv7 algorithm. The model was evaluated using both 2-dimensional and a novel in-house developed 2.5-dimensional approach. Performance measures included F1, Positive Predictive Value (PPV), Sensitivity, F1 curve, PPV-Sensitivity curve, Intersection over Union (IoU), and mean average PPV (mAP). RESULTS: A total of 326 patients with 1034 tumors with 7 different pathologies were analyzed across ten benchmarks. The average 2D evaluation results were as follows: Positive Predictive Value (PPV) of 0.69 ± 0.05, sensitivity of 0.39 ± 0.02, and F1 score of 0.43 ± 0.03. For the 2.5D evaluation, the average results included a PPV of 0.72 ± 0.06, sensitivity of 0.61 ± 0.06, and F1 score of 0.66 ± 0.04. The best model performance demonstrated a 2.5D PPV of 0.75, sensitivity of 0.69, and F1 score of 0.72. CONCLUSION: Using computer vision for tumor identification is a cutting-edge and rapidly expanding subject. In this work, we showed that YOLOv7 can be utilized in the detection of kidney cancers.

Imaging Characteristics of Clear Cell Papillary Renal Cell Carcinoma: Identifying the Sheep in Wolf's Clothing.

OBJECTIVE: This study aimed to describe the characteristics of computed tomography (CT) and magnetic resonance imaging (MRI) of clear cell papillary renal cell carcinoma (CCPRCC). METHODS: This retrospective study comprised 27 patients diagnosed with 29 tumors of CCPRCC. The study was approved by the Medical Ethics Committee and the requirement for the informed consent was waived. The inclusion criteria stipulated pathology-confirmed CCPRCCs with at least one preoperative imaging examination, including CT or MRI. Two experienced radiologists independently analyzed the imaging characteristics, including size, location, growth mode, morphology, texture, density, and enhancement pattern. Paired t-test was used to compare differences in CT Hounsfield unit values and apparent diffusion coefficient (ADC) imaging between the tumor and the renal cortex. RESULTS: The mean age of the 27 patients was 57.0 ± 14.2 years. Nineteen patients underwent CT, while 12 underwent MRI (There are 4 patients underwent not only CT but also MRI). Among the cases, 26 (96%) were single, and 1 (4%) was multiple, consisting of three lesions. Out of the 29 tumors, 15 (52%) were located in the left kidney and 14 (48%) in the right kidney. The mean tumor diameter was 3.3 ± 1.7 cm. Furthermore, 19 (66%), 3 (10%), and 7 (24%) tumors were solid, cystic, mixed solid, and cystic type, respectively. The growth mode was endogenous and exogenous in 8 (28%) and 21 (72%) tumors, respectively. The tumor shape was irregular and round in 5 (17%) and 24 (83%) tumors, respectively. The CT value of the tumor was approximately 33.2 ± 9.8 HU, which was not significantly different from that of the renal cortex(31.1 ± 6.3HU)(p = 0.343). Furthermore, 7 (24%), 12 (41%), and 3 (10%) had calcification, cystic degeneration, and hemorrhage, respectively. In 12 tumors, hypointense and hyperintense were predominant on T1 and T2-weighted images, respectively. The tumor capsule was found at the edge of 12 tumors. The average ADC value of the tumor (1.54 ± 0.74 × 10-3 mm2/s) and that of the renal cortex(1.68 ± 0.63×10-3mm2 /s) was not statistically significantly different (p = 0.260). The enhancement scanning revealed "wash-in and wash-out" enhancement in 19 (68%) tumors, continuous or progressive enhancement in 6 (21%) tumors, and enhanced cystic wall and central separation in 3 (11%) tumors. CONCLUSION: CCPRCC occurs more likely in middle-aged and elderly individuals, and the tumor is prone to cystic degeneration, with rare bleeding and calcification, and no obvious limitation on MRI diffusion-weighted imaging, which enhancement form performs as mainly "wash-in and washout," but the final diagnosis depends on histopathology.