Renal oncocytoma is a benign renal neoplasm which has mostly been reported in adults. Occurrence in children is infrequent. To date, there are only six pediatric cases of renal oncocytoma reported previously. Herein, we report a 13-year-old girl presented with hematuria for a week. Abdominal computed tomography showed a well-defined heterogeneous solid mass with a stellate central scar in the left kidney. The patient underwent a nephron sparing surgery. Histopathological and immunohistochemical findings confirmed the diagnosis of renal oncocytoma. Though uncommon, renal oncocytoma should be considered as the differential diagnosis of renal tumor in children. In addition, intranuclear inclusions were firstly described in this pediatric patient with unclear significance, which need a large cohort to summarize and analyze.

BACKGROUND: This study investigates the proteomic landscapes of chromophobe renal cell carcinoma (chRCC) and renal oncocytomas (RO), two subtypes of renal cell carcinoma that together account for approximately 10% of all renal tumors. Despite their histological similarities and shared origins, chRCC is a malignant tumor necessitating aggressive intervention, while RO, a benign growth, is often subject to overtreatment due to difficulties in accurate differentiation.
METHODS: We conducted a label-free quantitative proteomic analysis on solid biopsies of chRCC (n = 5), RO (n = 5), and normal adjacent tissue (NAT, n = 5). The quantitative analysis was carried out by comparing protein abundances between tumor and NAT specimens. Our analysis identified a total of 1610 proteins across all samples, with 1379 (85.7%) of these proteins quantified in at least seven out of ten LC‒MS/MS runs for one renal tissue type (chRCC, RO, or NAT).
RESULTS: Our findings revealed significant similarities in the dysregulation of key metabolic pathways, including carbohydrate, lipid, and amino acid metabolism, in both chRCC and RO. Compared to NAT, both chRCC and RO showed a marked downregulation in gluconeogenesis proteins, but a significant upregulation of proteins integral to the citrate cycle. Interestingly, we observed a distinct divergence in the oxidative phosphorylation pathway, with RO showing a significant increase in the number and degree of alterations in proteins, surpassing that observed in chRCC.
CONCLUSIONS: This study underscores the value of integrating high-resolution mass spectrometry protein quantification to effectively characterize and differentiate the proteomic landscapes of solid tumor biopsies diagnosed as chRCC and RO. The insights gained from this research offer valuable information for enhancing our understanding of these conditions and may aid in the development of improved diagnostic and therapeutic strategies.

To investigate different radiomics models based on single phase and the different phase combinations of radiomics features from 3D tri-phasic CT to distinguish RO from chRCC.
A total of 96 patients (30 RO and 66 chRCC) were enrolled in this study. Radiomics features were extracted from unenhanced phase (UP), corticomedullary phase (CMP), and nephrographic phase (NP) CT images. Feature selection was based on the least absolute shrinkage and selection operator regression (LASSO) method. The selected features were used to develop different radiomics models using logistic regression (LR) analysis, including model 1 (UP), model 2(CMP), model 3(NP), model 4(UP+CMP), model 5(UP+NP), model 6(CMP+NP), and model 7(UP+CMP+NP). The radiomics model demonstrating the highest discrimination performance was utilized to construct the combined model (model 8) with clinical factors. A nomogram based on the model 8 was established. To evaluate the diagnostic performance of the different models, the receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used. Delong\'s test was utilized to assess the statistical significance of the AUC improvement across the models.
Among the seven radiomics models, model 7 exhibited the highest AUC of 0.84 (95% CI 0.69, 0.99), and model 7 demonstrated a significantly superior AUC compared to the other radiomics models (all P < 0.05). The AUC values of radiomics models based on two phases (model4, mode5, mode6) were greater than the models based on single phase (model1, mode2, mode3) (all P < 0.05). Model 3 illustrated the best performance of the three radiomics models based on single phase with an AUC of 0.76 (95% CI 0.57, 099). Model 6 illustrated the best performance of the three radiomics models based on two-phases combination with an AUC of 0.83 (0.66, 0.99). Model 8 achieved an AUC of 0.93 (95% CI 0.83, 1.00) which is higher than those all radiomics models.
Radiomics models based on combination of radiomics features from UP, CMP, and NP can be a useful and promising technique to differentiate RO from chRCC. Moreover, the model combining clinical factors and radiomics features showed better classification performance to distinguish them.

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The increasing evidence of oncocytic renal tumors positive in 99mTc Sestamibi Single Photon Emission Tomography/Computed Tomography (SPECT/CT) examination calls for the development of diagnostic tools to differentiate these tumors from more aggressive forms. This study combined radiomics analysis with the uptake of 99mTc Sestamibi on SPECT/CT to differentiate benign renal oncocytic neoplasms from renal cell carcinoma. A total of 57 renal tumors were prospectively collected. Histopathological analysis and radiomics data extraction were performed. XGBoost classifiers were trained using the radiomics features alone and combined with the results from the visual evaluation of 99mTc Sestamibi SPECT/CT examination. The combined SPECT/radiomics model achieved higher accuracy (95%) with an area under the curve (AUC) of 98.3% (95% CI 93.7-100%) than the radiomics-only model (71.67%) with an AUC of 75% (95% CI 49.7-100%) and visual evaluation of 99mTc Sestamibi SPECT/CT alone (90.8%) with an AUC of 90.8% (95%CI 82.5-99.1%). The positive predictive values of SPECT/radiomics, radiomics-only, and 99mTc Sestamibi SPECT/CT-only models were 100%, 85.71%, and 85%, respectively, whereas the negative predictive values were 85.71%, 55.56%, and 94.6%, respectively. Feature importance analysis revealed that 99mTc Sestamibi uptake was the most influential attribute in the combined model. This study highlights the potential of combining radiomics analysis with 99mTc Sestamibi SPECT/CT to improve the preoperative characterization of benign renal oncocytic neoplasms. The proposed SPECT/radiomics classifier outperformed the visual evaluation of 99mTc Sestamibii SPECT/CT and the radiomics-only model, demonstrating that the integration of 99mTc Sestamibi SPECT/CT and radiomics data provides improved diagnostic performance, with minimal false positive and false negative results.

Paraganglioma is a rare type of neuroendocrine tumor with variable clinical presentations, making diagnosis relatively challenging. In this report, we present a case of retroperitoneal paraganglioma in a patient who experienced intermittent episodes of dizziness and chest pain. Imaging studies conducted during the patient\'s hospitalization revealed the presence of a lesion in the upper region of the right kidney, as well as a mass in the left retroperitoneal area that was suspected to be a paraganglioma. Biochemical studies were collected, including 24-hour urine metanephrines, urine catecholamines, urine cortisol, plasma metanephrines, renin, and aldosterone. However, it took an extended period of time for these results to come back. Given high clinical suspicion, alpha-blockade was initiated without a definite diagnosis of paraganglioma. Ultimately, the patient underwent tumor resection and the final pathology confirmed paraganglioma. The pathology of the contralateral renal mass showed oncocytoma. This case serves as an illustration of the difficulties that can arise when diagnosing and treating undiagnosed paragangliomas within a community healthcare setting.

This is a case report about a patient which presents with two right renal tumors, one of them being an oncocytoma with typical histopathological features and renal vein extension. Recent studies show that despite renal vein thrombus being a histological sign of malignancy; when associated with renal oncocytoma, it should not alter the benign prognosis of oncocytoma, and a simple follow-up may be carried. Further explorations should be done when easily available, and when the histopathologic diagnosis of oncocytoma is uncertain, to rule out the differential diagnosis of a chromophobe renal cell carcinoma, oncocytic variant.

BACKGROUND: Benign renal tumors, such as renal oncocytoma (RO), can be erroneously diagnosed as malignant renal cell carcinomas (RCC), because of their similar imaging features. Computer-aided systems leveraging radiomic features can be used to better discriminate benign renal tumors from the malignant ones. The purpose of this work was to build a machine learning model to distinguish RO from clear cell RCC (ccRCC).
METHODS: We collected CT images of 77 patients, with 30 cases of RO (39%) and 47 cases of ccRCC (61%). Radiomic features were extracted both from the tumor volumes identified by the clinicians and from the tumor\'s zone of transition (ZOT). We used a genetic algorithm to perform feature selection, identifying the most descriptive set of features for the tumor classification. We built a decision tree classifier to distinguish between ROs and ccRCCs. We proposed two versions of the pipeline: in the first one, the feature selection was performed before the splitting of the data, while in the second one, the feature selection was performed after, i.e., on the training data only. We evaluated the efficiency of the two pipelines in cancer classification.
RESULTS: The ZOT features were found to be the most predictive by the genetic algorithm. The pipeline with the feature selection performed on the whole dataset obtained an average ROC AUC score of 0.87 ± 0.09. The second pipeline, in which the feature selection was performed on the training data only, obtained an average ROC AUC score of 0.62 ± 0.17.
CONCLUSIONS: The obtained results confirm the efficiency of ZOT radiomic features in capturing the renal tumor characteristics. We showed that there is a significant difference in the performances of the two proposed pipelines, highlighting how some already published radiomic analyses could be too optimistic about the real generalization capabilities of the models.

Artificial intelligence (AI)-based techniques are increasingly being explored as an emerging ancillary technique for improving accuracy and reproducibility of histopathological diagnosis. Renal cell carcinoma (RCC) is a malignancy responsible for 2% of cancer deaths worldwide. Given that RCC is a heterogenous disease, accurate histopathological classification is essential to separate aggressive subtypes from indolent ones and benign mimickers. There are early promising results using AI for RCC classification to distinguish between 2 and 3 subtypes of RCC. However, it is not clear how an AI-based model designed for multiple subtypes of RCCs, and benign mimickers would perform which is a scenario closer to the real practice of pathology. A computational model was created using 252 whole slide images (WSI) (clear cell RCC: 56, papillary RCC: 81, chromophobe RCC: 51, clear cell papillary RCC: 39, and, metanephric adenoma: 6). 298,071 patches were used to develop the AI-based image classifier. 298,071 patches (350 × 350-pixel) were used to develop the AI-based image classifier. The model was applied to a secondary dataset and demonstrated that 47/55 (85%) WSIs were correctly classified. This computational model showed excellent results except to distinguish clear cell RCC from clear cell papillary RCC. Further validation using multi-institutional large datasets and prospective studies are needed to determine the potential to translation to clinical practice.

Renal oncocytoma is usually detected incidentally. It can be considered as a renal cell carcinoma (RCC) on preoperative imaging. They usually present as small masses and usually look like benign tumors. Giant oncocytomas are rare. A 72-year-old male patient was seen in the outpatient department for left scrotal swelling. Ultrasound (US) showed a giant mass compatible with RCC in the right kidney which was incidentally detected. Abdominal computed tomography (CT) revealed a mass with an axial diameter of 167×146 mm, compatible with RCC, a heterogeneous mass of soft tissue density with central necrosis. There was no evidence of tumor thrombus in the right renal vein or inferior vena cava. Open radical nephrectomy was performed through an anterior subcostal incision. Pathological examination revealed a 17×15 cm renal oncocytoma. The patient was discharged on the sixth day postoperatively. Clinically or radiologically, renal oncocytoma and renal cell carcinoma usually cannot be distinguished, although oncocytoma may be suspected if a central scar with fibrous extensions is seen, the so-called \"spoke-wheel appearance\". The treatment decision should be made according to the clinical aspects. Radical/partial nephrectomy or thermal ablation can be considered as treatment options. In this article, we review the literature on the radiological and pathological features of renal oncocytoma.