Distinguishing Benign Renal Tumors with an Oncocytic Gene Expression (ONEX) Classifier

The evolving management of small renal masses

Small renal masses (SRMs) are a heterogeneous group of tumours with varying metastatic potential. The increasing use and improving quality of abdominal imaging have led to increasingly early diagnosis of incidental SRMs that are asymptomatic and organ confined. Despite improvements in imaging and the growing use of renal mass biopsy, diagnosis of malignancy before treatment remains challenging. Management of SRMs has shifted away from radical nephrectomy, with active surveillance and nephron-sparing surgery taking over as the primary modalities of treatment. The optimal treatment strategy for SRMs continues to evolve as factors affecting short-term and long-term outcomes in this patient cohort are elucidated through studies from prospective data registries. Evidence from rapidly evolving research in biomarkers, imaging modalities, and machine learning shows promise in improving understanding of the biology and management of this patient cohort.

Risk of malignancy in renal biopsy: A review

The risks of malignancy for cytologic categories in renal biopsy specimens differ from the risks in most other sites. There are obvious areas in which cytopathologists can do better at classifying these cases, and the routine use of immunohistochemistry and core-needle biopsy may improve the accuracy of the classification of these specimens.

Kidney Cancer Diagnosis and Surgery Selection by Machine Learning from CT Scans Combined with Clinical Metadata

Kidney cancers are one of the most common malignancies worldwide. Accurate diagnosis is a critical step in the management of kidney cancer patients and is influenced by multiple factors including tumor size or volume, cancer types and stages, etc. For malignant tumors, partial or radical surgery of the kidney might be required, but for clinicians, the basis for making this decision is often unclear. Partial nephrectomy could result in patient death due to cancer if kidney removal was necessary, whereas radical nephrectomy in less severe cases could resign patients to lifelong dialysis or need for future transplantation without sufficient cause. Using machine learning to consider clinical data alongside computed tomography images could potentially help resolve some of these surgical ambiguities, by enabling a more robust classification of kidney cancers and selection of optimal surgical approaches. In this study, we used the publicly available KiTS dataset of contrast-enhanced CT images and corresponding patient metadata to differentiate four major classes of kidney cancer: clear cell (ccRCC), chromophobe (chRCC), papillary (pRCC) renal cell carcinoma, and oncocytoma (ONC). We rationalized these data to overcome the high field of view (FoV), extract tumor regions of interest (ROIs), classify patients using deep machine-learning models, and extract/post-process CT image features for combination with clinical data. Regardless of marked data imbalance, our combined approach achieved a high level of performance (85.66% accuracy, 84.18% precision, 85.66% recall, and 84.92% F1-score). When selecting surgical procedures for malignant tumors (RCC), our method proved even more reliable (90.63% accuracy, 90.83% precision, 90.61% recall, and 90.50% F1-score). Using feature ranking, we confirmed that tumor volume and cancer stage are the most relevant clinical features for predicting surgical procedures. Once fully mature, the approach we propose could be used to assist surgeons in performing nephrectomies by guiding the choices of optimal procedures in individual patients with kidney cancer.

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

BACKGROUND

Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

METHODS

From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

RESULTS

After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

CONCLUSIONS

According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Differentiating Oncocytic Renal Tumors from Chromophobe Renal Cell Carcinoma: Comparison of Peak Early-phase Enhancement Ratio to Clinical Risk Factors and Rater Predictions

Background

Most surgically resected benign renal tumors are found to be oncocytomas or indolent hybrid oncocytic tumors, which are difficult to differentiate from chromophobe renal cell carcinoma (chRCC) on renal mass biopsy. Both often exhibit CD117⁺ staining.

Objective

To evaluate the ability of the peak early-phase enhancement ratio (PEER) to distinguish oncocytomas from chRCC and compare its discrimination to traditional clinical risk factors and blinded clinical raters.

Design setting and participants

This was a diagnostic case-control study of patients (2006-2020) with oncocytoma or chRCC according to surgical pathology.

Intervention

Partial or radical nephrectomy.

Outcome measurements and statistical analysis

Three clinical raters blinded to histology measured the PEER and the presence of stellate scar and predicted the final histology for each tumor. Averaged and individual PEER values were compared to surgical pathology and assessed for interobserver variability. Subanalyses were conducted for patients with confirmed CD117⁺ status.

Results and limitations

For the 76 patients identified, PEER was higher among the 32 (42.1%) oncocytomas than among the 44 (57.9%) chRCCs (median 0.81 vs 0.43; p < 0.001), with high correlation across raters (correlation coefficients ≥0.85). A PEER cutoff of <0.60 was strongly associated with identification of chRCC (OR 95.7 (95% CI 19.9-460.8), p < 0.001). In the overall and CD117⁺ cohorts, sensitivity was 93.2% and 97.0%, the negative predictive value was 90.3% and 95.5%, and the area under the receiver operating characteristic curve (AUC) on multivariable modeling was 95.0% and 98.1%, respectively. PEER outperformed models with clinical risk factors alone (AUC 70.4%) and histology predictions by three raters (AUC 51.6%, 62.5%, and 63.1%). Limitations include reliance on surgical pathology and inclusion of a mix of early contrast-enhanced phases.

Conclusions

PEER reliably differentiated benign renal oncocytomas and indolent hybrid tumors from malignant chRCC with excellent diagnostic performance. A diagnostic pathway with biopsy, CD117 staining, and PEER deserves further study to potentially avoid unnecessary surgery for oncocytic renal tumors.

Patient summary

We assessed a measurement called PEER on computed tomography (CT) scans and found higher values for benign and lower values for malignant kidney masses, so we were able to tell these apart. PEER was reliable for identifying tumors with positive staining for the CD117 protein biomarker as well as in the overall patient group. Our results show that PEER could be considered for use with biopsy and CD117 staining to potentially avoid unnecessary surgery for benign kidney masses.

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.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis

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.

Conclusion

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01099-0.

Do we need an updated classification of oncocytic renal tumors? : Emergence of low-grade oncocytic tumor (LOT) and eosinophilic vacuolated tumor (EVT) as novel renal entities

The category of "oncocytic renal tumors'' includes well-recognized entities, such as renal oncocytoma (RO) and eosinophilic variant of chromophobe renal cell carcinoma (eo-ChRCC), as well as a group of "gray zone" oncocytic tumors, with overlapping features between RO and eo-ChRCC that create ongoing diagnostic and classification problems. These types of renal tumors were designated in the past as "hybrid oncocytoma-chromophobe tumors". In a recent update, the Genitourinary Pathology Society (GUPS) proposed the term "oncocytic renal neoplasm of low malignant potential, not further classified", for such solitary and sporadic, somewhat heterogeneous, but relatively indolent tumors, with equivocal RO/eo-ChRCC features. GUPS also proposed that the term "hybrid oncocytic tumor" be reserved for tumors found in a hereditary setting, typically arising as bilateral and multifocal ones (as in Birt-Hogg-Dubé syndrome). More recent developments in the "gray zone" of oncocytic renal tumors revealed that potentially distinct entities may have been "hidden" in this group. Recent studies distinguished two new entities: "Eosinophilic Vacuolated Tumor" (EVT) and "Low-grade Oncocytic Tumor" (LOT). The rapidly accumulated evidence on EVT and LOT has validated the initial findings and has expanded the knowledge on these entities. Both are uniformly benign and are typically found in a sporadic setting, but rarely can be found in patients with tuberous sclerosis complex. Both have readily distinguishable morphologic and immunohistochemical features that separate them from similar renal tumors, without a need for detailed molecular studies. These tumors very frequently harbor TSC/MTOR mutations that are however neither specific nor restricted to these two entities. In this review, we outline a proposal for a working framework on how to classify such low-grade oncocytic renal tumors. We believe that such framework will facilitate their handling in practice and will stimulate further discussions and studies to fully elucidate their spectrum.

Molecular imaging in oncology: Current impact and future directions

The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging-particularly when combined with liquid biopsy for screening purposes-promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.

Decision-making in active surveillance in kidney cancer: current trends and future urine and tissue markers

Surveillance for small renal masses is a growing choice of management amongst physicians and patients. These decisions, however, can be difficult as patient factors and tumor factors may blur the line between continued surveillance and intervention. Currently, there are no biomarkers that are readily available to aid in the decision making for patients with known renal cell carcinoma; however, many show promise. We herein review the literature of the adjunct tools that are currently available for decision making in small renal masses, but also new potential biomarkers that can potentially be of use.