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.

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.

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.

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.

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.

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.

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.

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.

Enhancement of Tumor Perfusion and Antiangiogenic Therapy in Murine Models of Clear Cell Renal Cell Carcinoma Using Ultrasound-Stimulated Microbubbles.

OBJECTIVE: To explore the effect of ultrasound-stimulated microbubble cavitation (USMC) on enhancing antiangiogenic therapy in clear cell renal cell carcinoma. MATERIALS AND METHODS: We explored the effects of USMC with different mechanical indices (MIs) on tumor perfusion, 36 786-O tumor-bearing nude mice were randomly assigned into four groups: (i) control group, (ii) USMC0.25 group (MI = 0.25), (iii) USMC1.4 group (MI = 1.4) (iv) US1.4 group (MI = 1.4). Tumor perfusion was assessed by contrast-enhanced ultrasound (CEUS) before the USMC treatment and 30 min, 4h and 6h after the USMC treatment, respectively. Then we evaluated vascular normalization(VN) induced by low-MI (0.25) USMC treatment, 12 tumor-bearing nude mice were randomly divided into two groups: (i) control group (ii) USMC0.25 group. USMC treatment was performed, and tumor microvascular imaging and blood perfusion were analyzed by MicroFlow imaging (MFI) and CEUS 30 min after each treatment. In combination therapy, a total of 144 tumor-bearing nude mice were randomly assigned to six groups (n = 24): (i) control group, (ii) USMC1.4 group, (iii) USMC0.25 group, (iv) bevacizumab(BEV) group, (v) USMC1.4 +BEV group, (vi) USMC0.25 +BEV group. BEV was injected on the 6th, 10th, 14th, and 18th d after the tumors were inoculated, while USMC treatment was performed 24 h before and after every BEV administration. We examined the effects of the combination therapy through a series of experiments. RESULTS: Tumor blood perfusion enhanced by USMC with low MI (0.25)could last for more than 6h, inducing tumor VN and promoting drug delivery. Compared with other groups, USMC0.25+BEV combination therapy had the strongest inhibition on tumor growth, led to the longest survival time of the mice. CONCLUSION: The optimized USMC is a promising therapeutic approach that can be combined with antiangiogenic therapy to combat tumor progression.

CT-derived radiomics predict the growth rate of renal tumours in von Hippel-Lindau syndrome.

AIM: To predict renal tumour growth patterns in von Hippel-Lindau syndrome by utilising radiomic features to assist in developing personalised surveillance plans leading to better patient outcomes. MATERIALS AND METHODS: The study evaluated 78 renal tumours in 55 patients with histopathologically-confirmed clear cell renal cell carcinomas (ccRCCs), which were segmented and radiomics were extracted. Volumetric doubling time (VDT) classified the tumours into fast-growing (VDT <365 days) or slow-growing (VDT ≥365 days). Volumetric and diametric growth analyses were compared between the groups. Multiple logistic regression and random forest classifiers were used to select the best features and models based on their correlation and predictability of VDT. RESULTS: Fifty-five patients (mean age 42.2 ± 12.2 years, 27 men) with a mean time difference of 3.8 ± 2 years between the baseline and preoperative scans were studied. Twenty-five tumours were fast-growing (low VDT, i.e., <365 days), and 53 tumours were slow-growing (high VDT, i.e., ≥365 days). The median volumetric and diametric growth rates were 1.71 cm3/year and 0.31 cm/year. The best feature using univariate analysis was wavelet-HLL_glcm_ldmn (area under the receiver operating characteristic [ROC] curve [AUC] of 0.80, p<0.0001), and with the random forest classifier, it was log-sigma-0-5-mm-3D_glszm_ZonePercentage (AUC: 79). The AUC of the ROC curves using multiple logistic regression was 0.74, and with the random forest classifier was 0.73. CONCLUSION: Radiomic features correlated with VDT and were able to predict the growth pattern of renal tumours in patients with VHL syndrome.

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.

The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma.

OBJECTIVE: We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). METHODS: A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. RESULTS: The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. CONCLUSION: DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.

Application of Computed Tomography Scanning Parameters Combined with Serum Cripto-1 in the Diagnosis of Renal Cell Carcinoma.

OBJECTIVE: This study aims to analyse the clinical value of computed tomography (CT) scanning parameters combined with serum teratoma-derived growth factor-1 (Cripto-1) in the diagnosis of renal cell carcinoma (RCC). METHODS: A retrospective analysis was conducted on 256 patients with renal tumour admitted to our hospital from July 2020 to December 2022. They were divided into malignant group (n = 180) and benign group (n = 76) based on the final pathological results. All subjects underwent CT scans and serum Cripto-1 testing. The CT signs and serum Cripto-1 levels of the patients were analysed, and their diagnostic efficacy was evaluated. RESULTS: The pathological diagnosis results showed 180 cases of malignant tumours, including 73 cases of clear cell carcinoma, 60 cases of papillary RCC and 47 cases of chromophobe cell carcinoma as well as 76 cases of benign tumour, including 31 cases of renal angiomyolipoma, 25 cases of eosinophilic tumour and 20 cases of renal fibroma. The malignant group had significantly higher incidence of cystic necrosis, uneven enhancement and rapid progression than the benign group (p < 0.01). The incidence of calcification was not statistically different between the two groups (p > 0.05). The malignant group had lower CT value of focus (p < 0.01) and relative corrected CT value of the renal cortex (p < 0.05), and significantly higher serum levels of Cripto-1 (p < 0.01) than the malignant group. The area under the curve of the combined diagnosis was significantly higher than that of serum Cripto-1 alone and comprehensive diagnosis of CT parameters (pcombined diagnosis vs serum Cripto-1 < 0.001, pcombined diagnosis vs comprehensive diagnosis of CT parameters = 0.002). The sensitivity of the combined diagnosis was also higher than that of serum Cripto-1 and CT parameters alone. CONCLUSIONS: The combination of CT scanning parameters and serum Cripto-1 has high value in the diagnosis of renal tumours, and the area under the curve and sensitivity of the combined diagnosis are high. This work provides reference for clinical diagnosis and treatment of renal tumours.

Role of PSMA-targeted PET-CT in renal cell carcinoma: a systematic review and meta-analysis.

Fluoro-deoxy glucose positron emission tomography/computed tomography (PET/CT), the workhorse of nuclear medicine, has limited utility for renal cell carcinoma (RCC), particularly clear cell variant. Thus, various other tracers have been tried for evaluation of RCC. One of the most promising targets for radiotracers is prostate-specific membrane antigen (PSMA) expressed in abundance in carcinoma-associated neo-vasculature. Thus, we tried to review and analyse the role of PSMA-targeted PET/CT in evaluation of RCC. Databases like PubMed, EMBASE and SCOPUS were searched for original studies published on PSMA-targeted PET/CT in RCC till 30 September 2023. Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the included studies. Pooled sensitivity and specificity were calculated and represented with 95% confidence intervals (95%CI). Heterogeneity in the studies was assessed by I-square index. Pooled sensitivity and specificity of PSMA-targeted PET/CT for detection of local disease estimates were 87.2% (95%CI: 77-94%) and 100% (95%CI: 92.9-100%), respectively. Pooled sensitivity and specificity for detection of local recurrent disease are 100% (95%CI: 71.5-100%) and 100% (95%CI: 89.4-100%), respectively. Pooled sensitivity and specificity for detection of metastatic disease are 92% (95%CI: 86.2-96%) and 96.9% (95%CI: 83.8-99.9%), respectively. Pooled sensitivity of PSMA-targeted PET/CT for detection of clear cell renal cell carcinoma (ccRCC) and non-ccRCC are 94.7% (95%CI: 88-98.3%) and 75% (95%CI: 35-96.8%), respectively. PSMA-targeted PET-CT demonstrated better diagnostic efficacy for the detection of recurrent RCC. Whilst for staging RCC, it had higher specificity but lower sensitivity. Thus, it can serve as a non-invasive adjuvant tool to conventional imaging in the evaluation of staging of RCC, particularly clear cell variant.

Qualitative and quantitative characteristics of CEUS for renal cell carcinoma and angiomyolipoma: a narrative review.

Incidental findings of renal masses are increasing. However, a substantial portion of surgically treated renal masses turn out to be benign on histopathological examination. Thus, there is a clear need for improved pre-surgical assessment to minimize unnecessary invasive procedures. The challenge intensifies when distinguishing between renal cell carcinoma (RCC) and angiomyolipoma (AML) in renal lesions smaller than 4 cm with minimal adipose tissue. In such cases, contrast-enhanced ultrasound (CEUS) has emerged as a valuable diagnostic tool, by utilizing both qualitative and quantitative parameters. Quantitative measures offer objectivity, reliability, and reproducibility compared to qualitative parameters, enabling the characterization of RCC subtypes and differentiation from AML. Qualitative features as enhancement pattern, degree, and peak were less helpful in distinguishing triphasic minimal fat AML (TAML) from epithelioid AML (EAML), with the pseudocapsule sign potentially being the only distinguishing qualitative feature. The pseudocapsule sign was more frequently observed in ccRCCs (38.0%) than in AMLs (15.6%). Moreover, it was detected in 40.0% of EAMLs and 34.5% of ccRCCs but not in TAMLs due to similar growth patterns between EAMLs and low-grade ccRCCs. Quantitative measures such as the time-to-peak (TTP) ratio can further enhance diagnostic accuracy and also TOC ratio should be considered, as it was higher in clear cell RCCs (ccRCCs) and in EAMLs compared to TAMLs, indicating behavior similar to ccRCCs. However, CEUS remains an operator-dependent exam.

Predictive Model of CK7 Expression in Patients With Clear Cell Renal Cell Carcinoma by Combined Multimodal Ultrasound Diagnostic Techniques: A Retrospective Study.

OBJECTIVE: The aim of the work described here was to develop and validate a predictive model for cytokeratin 7 (CK7) expression in clear cell renal cell carcinoma (ccRCC) patients by combining multimodal ultrasound diagnostic techniques. METHODS: This retrospective study enrolled 157 surgically confirmed ccRCC patients. All patients underwent pre-operative multimodal ultrasound diagnostic examinations, including B-mode ultrasound (US), color Doppler flow imaging (CDFI) and contrast-enhanced ultrasound (CEUS). The patients were randomly divided into a training group (103 cases) and a testing group (54 cases). Univariate and multivariate logistic regression analyses were performed in the training group to identify independent indicators associated with CK7 positivity. These indicators were included in the predictive model. Receiver operating characteristic (ROC) curves and calibration curves were used to evaluate the model's discriminative ability and accuracy. Decision curve analysis (DCA) and nomogram visualization were used to assess the clinical utility of the predictive model. RESULTS: Univariate logistic regression analysis revealed that US and CDFI observations were not correlated with CK7 expression and could not predict it. Multivariate logistic regression analysis identified age (odds ratio [OR] = 0.953, 95% confidence interval [CI]: 0.909-0.999), wash-in pattern (OR = 0.180, 95% CI: 0.063-0.513) and enhancement homogeneity (OR = 11.610, 95% CI: 1.394-96.675) as independent factors related to CK7 positivity in ccRCC. Incorporating these variables into the predictive model resulted in areas under the receiver operating characteristic curve of 0.812 (95% CI: 0.711-0.913) for the training group and 0.792 (95% CI: 0.667-0.924) for the testing group. The calibration curve and DCA revealed that the model had good accuracy and clinical utility of the model. CONCLUSION: The combination of multimodal ultrasound diagnostic techniques in constructing a predictive model for CK7 expression in ccRCC patients has significant predictive value.

A pilot study of [68Ga]Ga-fibroblast activation protein inhibitor-04 PET/CT in renal cell carcinoma.

OBJECTIVES: As a promising positron emission tomography (PET) tracer, [68Ga]Ga-fibroblast activation protein inhibitor-04([68Ga]Ga-FAPI-04) performs better than 2-[18F]fluoro-2-deoxy-d-glucose ([18F]FDG) at diagnosing primary and metastatic lesions in patients with various types of cancer. We investigated the utility of [68Ga]Ga-FAPI-04 PET/CT for the detection of primary and metastatic lesions in renal cell carcinoma (RCC). [18F]FDG PET/CT were used for comparison. METHODS: Twenty-two patients with suspected RCC or recurrent RCC were enrolled in our study. Among these patients, 14 were newly diagnosed with RCC, 3 had recurrent RCC, and 5 were excluded from further analysis due to having benign renal tumours. Seventeen patients with RCC underwent [68Ga]Ga-FAPI-04 PET/CT, and 6 of them also received [18F]FDG PET/CT. The positive detection rates were calculated and compared with those in patients who underwent both scans. RESULTS: Data from 17 patients with RCC (median age: 60.5 years, interquartile range [IQR]: 54-70 years) were evaluated. The positive detection rate of [68Ga]Ga-FAPI-04 PET/CT for RCC was 64.7% (11/17). Lymph node metastases (n = 44), lung metastasis (n = 1), and bone metastasis (n = 1) were detected. Six patients with RCC underwent [68Ga]Ga-FAPI-04 and [18F]FDG PET/CT. [68Ga]Ga-FAPI-04 PET/CT showed a higher positive detection rate than [18F]FDG PET/CT in detecting RCC (83.3% [5/6] vs. 50% [3/6], P = 0.545). Additionally, [68Ga]Ga-FAPI-04 PET/CT has higher SUVmax (3.20 [IQR: 2.91-5.80 vs. 2.71 [IQR: 2.13-3.10], P = 0.116) and tumour-to-background ratio (TBR) values (1.60 [IQR: 1.33-3.67] vs. 0.86 [0.48-1.21], P = 0.028) than [18F]FDG PET/CT. CONCLUSIONS: These findings suggest that [68Ga]Ga-FAPI-04 PET/CT has potential value in RCC diagnosis. Further studies are warranted to validate these results. ADVANCES IN KNOWLEDGE: Clinical utility of [68Ga]Ga-FAPI-04 in RCC remains unclear, and there are not many similar studies in the literature. We evaluated the role of [68Ga]Ga-FAPI-04 in diagnosing RCC.