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.

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.

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.

To evaluate whether significant loss in ipsilateral renal parenchymal volume (IRPV) and renal function occurs during active surveillance (AS) of renal oncocytoma (RO) patients.
Renal function (estimated glomerular filtration rate, eGFR) dynamics were retrospectively analyzed in 32 consecutive biopsy-diagnosed RO patients managed with AS at a National Comprehensive Cancer Network institute. Three-dimensional kidney and tumor reconstructions were generated and IRPV was calculated using volumetry software (Myrian®) for all patients with manually estimated RO growth >+10 cm3. GFR and IRPV were compared at AS initiation vs. the last follow-up using 2-sided paired t-tests. The correlation between change in IRPV and change in RO size or GFR was tested using a Spearman coefficient.
With median follow-up of 37 months, there was no significant change between initial vs. last eGFR (median 71.0 vs. 70.5 ml/min/1.73 m2, P = 0.50; median change -3.0 ml/min/1.73 m2). Among patients (n = 17) with RO growth >+10 cm3 during AS (median growth +28.6 cm3, IQR +16.9- + 46.5 cm3), IRPV generally remained stable (median change +0.5%, IQR -1.2%- + 1.2%), with only 2 cases surpassing 5% loss. No IRPV loss was detected among any patient within the top tertile of RO growth magnitude. RO growth magnitude did not correlate with loss of either IRPV (ρ = -0.30, P = 0.24) or eGFR (ρ = -0.16, P = 0.40), including among patient subsets with lower initial eGFR. Study limitations include a lack of long-term follow-up.
Volumetry is a promising novel tool to measure kidney and tumor tissue changes during AS. Our study using volumetry indicates that clinically significant loss of IRPV or eGFR is uncommon and unrelated to tumor growth among untreated RO patients with intermediate follow-up. These findings support that AS is in general functionally safe for RO patients, however longer study is needed to determine safety durability, particularly among uncommon ≥cT2 RO variants.

BACKGROUND: Correct tumor subtyping of primary renal tumors is essential for treatment decision in daily routine. Most of the tumors can be classified based on morphology alone. Nevertheless, some diagnoses are difficult, and further investigations are needed for correct tumor subtyping. Besides histochemical investigations, high-mass-resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) can detect new diagnostic biomarkers and hence improve the diagnostic.
METHODS: Formalin-fixed paraffin embedded tissue specimens from clear cell renal cell carcinoma (ccRCC, n = 552), papillary renal cell carcinoma (pRCC, n = 122), chromophobe renal cell carcinoma (chRCC, n = 108), and renal oncocytoma (rO, n = 71) were analyzed by high-mass-resolution MALDI fourier-transform ion cyclotron resonance (FT-ICR) MSI. The SPACiAL pipeline was executed for automated co-registration of histological and molecular features. Pathway enrichment and pathway topology analysis were performed to determine significant differences between RCC subtypes.
RESULTS: We discriminated the four histological subtypes (ccRCC, pRCC, chRCC, and rO) and established the subtype-specific pathways and metabolic profiles. rO showed an enrichment of pentose phosphate, taurine and hypotaurine, glycerophospholipid, amino sugar and nucleotide sugar, fructose and mannose, glycine, serine, and threonine pathways. ChRCC is defined by enriched pathways including the amino sugar and nucleotide sugar, fructose and mannose, glycerophospholipid, taurine and hypotaurine, glycine, serine, and threonine pathways. Pyrimidine, amino sugar and nucleotide sugar, glycerophospholipids, and glutathione pathways are enriched in ccRCC. Furthermore, we detected enriched phosphatidylinositol and glycerophospholipid pathways in pRCC.
CONCLUSIONS: In summary, we performed a classification system with a mean accuracy in tumor discrimination of 85.13%. Furthermore, we detected tumor-specific biomarkers for the four most common primary renal tumors by MALDI-MSI. This method is a useful tool in differential diagnosis and biomarker detection.

BACKGROUND: To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7).
METHODS: In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7.
RESULTS: A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson\'s correlation coefficient was statistically significant between Rad-score and CK7.
CONCLUSIONS: We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision.

UNASSIGNED: 99mTc-Sestamibi Single Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) contributes to the non-invasive differentiation of renal oncocytoma (RO) from renal cell carcinoma (RCC) by characterising renal tumours as Sestamibi positive or Sestamibi negative regarding their 99mTc-Sestamibi uptake compared to the non-tumoral renal parenchyma.
UNASSIGNED: To determine whether 99mTc- Sestamibi uptake in renal tumour and the non-tumoral renal parenchyma measured using Standard Uptake Value (SUV) SPECT, has a beneficial role in differentiating RO from RCC.
UNASSIGNED: Fifty-seven renal tumours from 52 patients were evaluated. In addition to visual evaluation of 99mTc-Sestamibi uptake, SUVmax measurements were performed in the renal tumour and the ipsilateral non-tumoral renal parenchyma. Analysis of the area under the receiver operating characteristic curve identified an optimal cut-off value for detecting RO, based on the relative ratio of 99mTc- Sestamibi uptake.
UNASSIGNED: Semiquantitative evaluation of 99mTc-Sestamibi uptake did not improve the performance of 99mTc- Sestamibi SPECT/CT in detecting RO. 99mTc- Sestamibi SPECT/CT identifies a group of mostly indolent Sestamibi-positive tumours with low malignant potential containing RO, Low-Grade Oncocytic Tumours, Hybrid Oncocytic Tumours, and a subset of chromophobe RCCs.
UNASSIGNED: The imaging limitations for accurate differentiation of Sestamibi-positive renal tumours mirror the recognised diagnostic complexities of the histopathologic evaluation of oncocytic neoplasia. Patients with Sestamibi-positive renal tumours could be better suited for biopsy and follow-up, according to the current active surveillance protocols.

Renal tumors with oncocytic or chromophobe-like morphology can be a common source of diagnostic difficulty. In some series, they constitute the largest group of unclassified renal cell carcinomas, a term used for neoplasms that do not fit the current classification of renal tumors. We describe the histological, immunohistochemical, and molecular findings of an eosinophilic renal neoplasm which presented with rib and liver metastases, and provide a review of the literature. The possibility of a renal oncocytoma with metastases was initially considered but excluded on the basis of several morphological and immunohistochemical features. Additionally, the tumor did not correspond with other traditional or newly emerging categories of renal neoplasms. It was therefore regarded as an unclassified oncocytic renal neoplasm which demonstrated evidence of malignant potential due to the presence of multiple metastases.