In Situ Metabolomics Expands the Spectrum of Renal Tumours Positive on 99mTc-sestamibi Single Photon Emission Computed Tomography/Computed Tomography Examination

CARRoT: R-package for predictive modelling by means of regression, adjusted for multiple regularisation methods

We present an R-package for predictive modelling, CARRoT (Cross-validation, Accuracy, Regression, Rule of Ten). CARRoT is a tool for initial exploratory analysis of the data, which performs exhaustive search for a regression model yielding the best predictive power with heuristic 'rules of thumb' and expert knowledge as regularization parameters. It uses multiple hold-outs in order to internally validate the model. The package allows to take into account multiple factors such as collinearity of the predictors, event per variable rules (EPVs) and R-squared statistics during the model selection. In addition, other constraints, such as forcing specific terms and restricting complexity of the predictive models can be used. The package allows taking pairwise and three-way interactions between variables into account as well. These candidate models are then ranked by predictive power, which is assessed via multiple hold-out procedures and can be parallelised in order to reduce the computational time. Models which exhibited the highest average predictive power over all hold-outs are returned. This is quantified as absolute and relative error in case of continuous outcomes, accuracy and AUROC values in case of categorical outcomes. In this paper we briefly present statistical framework of the package and discuss the complexity of the underlying algorithm. Moreover, using CARRoT and a number of datasets available in R we provide comparison of different model selection techniques: based on EPVs alone, on EPVs and R-squared statistics, on lasso regression, on including only statistically significant predictors and on stepwise forward selection technique.

A pilot radiometabolomics integration study for the characterization of renal oncocytic neoplasia

Differentiating benign renal oncocytic tumors and malignant renal cell carcinoma (RCC) on imaging and histopathology is a critical problem that presents an everyday clinical challenge. This manuscript aims to demonstrate a novel methodology integrating metabolomics with radiomics features (RF) to differentiate between benign oncocytic neoplasia and malignant renal tumors. For this purpose, thirty-three renal tumors (14 renal oncocytic tumors and 19 RCC) were prospectively collected and histopathologically characterised. Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) was used to extract metabolomics data, while RF were extracted from CT scans of the same tumors. Statistical integration was used to generate multilevel network communities of -omics features. Metabolites and RF critical for the differentiation between the two groups (delta centrality > 0.1) were used for pathway enrichment analysis and machine learning classifier (XGboost) development. Receiver operating characteristics (ROC) curves and areas under the curve (AUC) were used to assess classifier performance. Radiometabolomics analysis demonstrated differential network node configuration between benign and malignant renal tumors. Fourteen nodes (6 RF and 8 metabolites) were crucial in distinguishing between the two groups. The combined radiometabolomics model achieved an AUC of 86.4%, whereas metabolomics-only and radiomics-only classifiers achieved AUC of 72.7% and 68.2%, respectively. Analysis of significant metabolite nodes identified three distinct tumour clusters (malignant, benign, and mixed) and differentially enriched metabolic pathways. In conclusion, radiometabolomics integration has been presented as an approach to evaluate disease entities. In our case study, the method identified RF and metabolites important in differentiating between benign oncocytic neoplasia and malignant renal tumors, highlighting pathways differentially expressed between the two groups. Key metabolites and RF identified by radiometabolomics can be used to improve the identification and differentiation between renal neoplasms.

Machine Learning Integrating 99mTc Sestamibi SPECT/CT and Radiomics Data Achieves Optimal Characterization of Renal Oncocytic Tumors

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.

99mTc-Sestamibi SPECT/CT and histopathological features of oncocytic renal neoplasia

BACKGROUND

^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.

PURPOSE

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.

MATERIAL AND METHODS

Fifty-seven renal tumours from 52 patients were evaluated. In addition to visual evaluation of ^99mTc-Sestamibi uptake, SUV_max 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.

RESULTS

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.

CONCLUSION

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.

Low-grade oncocytic tumor (LOT) - a new renal entity ready for a prime time: An updated review

Low-grade oncocytic tumor (LOT) of kidney has been recently proposed as a new renal entity. LOT was identified in the spectrum of oncocytic renal tumors with overlapping features between oncocytoma and eosinophilic chromophobe renal cell carcinoma, or it has been labelled as one of those entities in prior studies and in practice. LOT is often a single, relatively small tumor, found in a non-syndromic setting, but rare examples of multiple LOTs or admixed with other tumors have been found in patients with tuberous sclerosis complex. LOT typically has solid architecture, and it is composed of eosinophilic cells, with round to oval 'low-grade' nuclei, lacking irregularities and showing focal perinuclear halos. Sharp transition into edematous stromal areas, with scattered or loosely arranged cells are frequently found. LOT has a consistent immunohistochemical profile with diffuse reactivity for cytokeratin 7 and absent (or rarely weak) expression for CD117, a profile different from oncocytoma and eosinophilic chromophobe renal cell carcinoma. Similarly, in contrast to those entities, it also lacks or shows only weak expression for FOXI1. Recent studies have shown that LOT has a molecular/genetic profile different from other renal tumors, with frequent alterations affecting the MTOR/TSC pathway genes. LOT demonstrates either disomic pattern or deletions of 19p13, 19q13 and 1p36, and lacks complete chromosomal losses or gains. In all published studies to date, LOT has shown benign behavior. In this review, we summarize the evidence from recently published studies, which strongly supports the conclusion that LOT is a distinct and unique renal entity.

Low-Grade Oncocytic Tumor of Kidney (CK7-Positive, CD117-Negative): Incidence in a single institutional experience with clinicopathological and molecular characteristics

Low-grade oncocytic tumor of the kidney (LOT) is characterized by cytoplasmic eosinophilia and a CK7-positive/CD117-negative immunophenotype. Morphologically, they exhibit overlapping features with oncocytoma and chromophobe renal cell carcinoma. Our aim was to obtain long-term clinical follow-up data, clinicopathological and molecular characteristics, and incidence of LOT. Tissue microarrays were constructed from 574 tumors historically diagnosed as oncocytoma and surgically treated at Mayo Clinic between 1970 and 2012, and immunostained for CK7 and CD117. An extended immunophenotype was obtained on whole slide sections, along with FISH for CCND1 rearrangement status and chromosomal microarray for copy number status. In addition, two cases were retrospectively identified in a set of tuberous sclerosis complex (TSC)-associated neoplasms and three more cases diagnosed on needle core biopsies were obtained during routine clinical practice. Twenty-four cases of LOT were identified among 574 consecutive tumors diagnosed as oncocytoma and treated with partial or radical nephrectomy, corresponding to an incidence of 4.18% of tumors historically diagnosed as oncocytomas, and 0.35% of 6944 nephrectomies performed between 1970 and 2012. Overall, 29 cases of LOT were identified in three clinical settings: sporadic, TSC-associated, and end-stage renal disease (ESRD). Multifocality was seen only in the setting of TSC and ESRD. No metastases attributable to LOT were identified (median follow-up 9.6 years). There were no recurrent arm level copy number changes detected by chromosomal microarray and all tested cases were negative for CCND1 rearrangement by FISH. LOT is an uncommon eosinophilic renal neoplasm with an indolent prognosis that constitutes ∼4% of tumors historically diagnosed as oncocytoma. The morphologic, immunophenotypic, and molecular features of this neoplasm suggest it is a distinct entity of renal neoplasia.