Advances in Imaging-Based Biomarkers in Renal Cell Carcinoma: A Critical Analysis of the Current Literature

CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses

Objective

This study aimed to explore the value of a radiomic nomogram based on contrast-enhanced computed tomography (CECT) for differentiating benign and malignant solid-containing renal masses.

Materials and Methods

A total of 122 patients with pathologically confirmed benign (n=47) or malignant (n=75) solid-containing renal masses were enrolled in this study. Radiomic features were extracted from the arterial, venous and delayed phases and further analysed by dimensionality reduction and selection. Four mainstream machine learning algorithm training models, namely, support vector machine (SVM), k-nearest neighbour (kNN), light gradient boosting (LightGBM) and logistic regression (LR), were constructed to determine the best classifier model. Univariate and multivariate analyses were used to determine the best clinical characteristics for constructing a clinical model. The radiomic and clinical signatures were integrated to construct a combined radiomic nomogram model. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the performance of the radiomic nomogram, radiomic signature, and clinical model.

Results

Thirteen radiomic features were selected for the development of the radiomic signature. Among the various radiomic models, the LR model demonstrated superior predictive efficiency and robustness, yielding an AUC of 0.952 in the training cohort and 0.887 in the test cohort. The AUC for the clinical model was 0.854 in the training cohort and 0.747 in the test cohort. Furthermore, the radiomic nomogram, which incorporated sex, age, alcohol consumption history, and the radiomic signature, exhibited excellent discriminative performance, yielding an AUC of 0.973 in the training cohort and 0.900 in the test cohort.

Conclusion

The radiomic nomogram based on CECT offers a promising and noninvasive approach for distinguishing malignant from benign solid renal masses. This tool can be used to guide treatment strategies effectively and can provide valuable insights for clinicians.

Clinical Implications of Basic Research: Exploring the Transformative Potential of Spatial 'Omics in Uro-oncology

New spatial molecular technologies are poised to transform our understanding and treatment of urological cancers. By mapping the spatial molecular architecture of tumours, these platforms uncover the complex heterogeneity within and around individual malignancies, offering novel insights into disease development, progression, diagnosis, and treatment. They enable tracking of clonal phylogenetics in situ and immune-cell interactions in the tumour microenvironment. A whole transcriptome/genome/proteome-level spatial analysis is hypothesis generating, particularly in the areas of risk stratification and precision medicine. Current challenges include reagent costs, harmonisation of protocols, and computational demands. Nonetheless, the evolving landscape of the technology and evolving machine learning applications have the potential to overcome these barriers, pushing towards a future of personalised cancer therapy, leveraging detailed spatial cellular and molecular data. PATIENT SUMMARY: Tumours are complex and contain many different components. Although we have been able to observe some of these differences visually under the microscope, until recently, we have not been able to observe the genetic changes that underpin cancer development. Scientists are now able to explore molecular/genetic differences using approaches such as "spatial transcriptomics" and "spatial proteomics", which allow them to see genetic and cellular variation across a region of normal and cancerous tissue without destroying the tissue architecture. Currently, these technologies are limited by high associated costs, and a need for powerful and complex computational analysis workflows. Future advancements and results through these new technologies may assist patients and their doctors as they make decisions about treating their cancer.

Radiogenomics Pilot Study: Association Between Radiomics and Single Nucleotide Polymorphism-Based Microarray Copy Number Variation in Diagnosing Renal Oncocytoma and Chromophobe Renal Cell Carcinoma

RO and ChRCC are kidney tumours with overlapping characteristics, making differentiation between them challenging. The objective of this research is to create a radiogenomics map by correlating radiomic features to molecular phenotypes in ChRCC and RO, using resection as the gold standard. Fourteen patients (6 RO and 8 ChRCC) were included in the prospective study. A total of 1,875 radiomic features were extracted from CT scans, alongside 632 cytobands containing 16,303 genes from the genomic data. Feature selection algorithms applied to the radiomic features resulted in 13 key features. From the genomic data, 24 cytobands highly correlated with histology were selected and cross-correlated with the radiomic features. The analysis identified four radiomic features that were strongly associated with seven genomic features. These findings demonstrate the potential of integrating radiomic and genomic data to enhance the differential diagnosis of RO and ChRCC, paving the way for more precise and non-invasive diagnostic tools in clinical practice.

Diagnostic Value of Contrast-Enhanced Ultrasound in Differentiating Malignant from Benign Small Renal Masses After CT/MRI

Background: The aim of this study was to assess the diagnostic performance of contrast-enhanced ultrasound (CEUS) in characterizing small renal masses (SRMs) measuring less than 3 cm and in distinguishing between malignant and benign SRMs. Methods: A retrospective study was conducted between January 2022 and January 2023 at the Radiology Department of (Anonymized data), with a total of 43 patients assessed via CT and MRI scans, which were subsequently studied by experienced radiologists who were blinded to the pathology results. The CEUS findings were then compared with histopathological examination outcomes or follow-up imaging results. Results: The study results revealed a notably high level of diagnostic accuracy, with sensitivity at 0.875, specificity at 0.94, positive predictive value at 0.95, and negative predictive value at 0.86 for characterizing SRMs. Spearman rank correlation analysis substantiated a robust positive linear correlation between the CEUS findings and biopsy results (r = 0.972). Conclusions: These findings underscore the potential utility of CEUS as a valuable tool for discriminating between malignant and benign SRMs, carrying significant implications for clinical decision-making and leading to improved patient outcomes. However, larger validation studies are imperative to establish its role in routine clinical practice and to address potential limitations.

Detection and Validation of Organic Metabolites in Urine for Clear Cell Renal Cell Carcinoma Diagnosis

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70-80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model.

METHODS

This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers.

RESULTS

The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity.

CONCLUSIONS

This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise.

Computed Tomography-Based Radiomics to Predict FOXM1 Expression and Overall Survival in Patients with Clear Cell Renal Cell Carcinoma

RATIONALE AND OBJECTIVES

To establish a computed tomography (CT)-based radiomics model to predict Fork head box M1(FOXM1) expression levels and develop a combined model for prognostic prediction in patients with clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

A total of 529 patients were utilized to assess the prognostic significance of FOXM1 expression and were subsequently categorized into low and high FOXM1 expression groups. 184 patients with CT images were randomly divided into training and validation cohorts. Radiomics signature (Rad-score) for predicting FOXM1 expression level was developed in the training cohort. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. A clinical model based on clinical factors and a combined model incorporating clinical factors and Rad-score were developed to predict ccRCC prognosis using Cox regression analyses. The concordance index(C-index) was employed to assess and compare the predictive capabilities of the Rad-score, TNM stage, clinical model, and combined model. The likelihood ratio test was used to compare the models' performance.

RESULTS

The Rad-score demonstrated high predictive accuracy for high FOXM1 expression with areas under the ROC curves of 0.713 and 0.711 in the training and validation cohorts. In the training cohort, the C-indexes for the Rad-score, TNM Stage, clinical model, and combined model were 0.657, 0.711, 0.737, and 0.741, respectively. Correspondingly, in the validation cohort, the C-indexes were 0.670, 0.712, 0.736, and 0.745. The combined model had the highest C-index, significantly outperforming the other models.

CONCLUSION

The Rad-score accurately predicts FOXM1 expression levels and is an independent prognostic factor for ccRCC.

PET/Computed Tomography Transformation of Oncology: Kidney and Urinary Tract Cancers

Renal cell carcinoma (RCC) and urothelial carcinoma (UC) are two of the most common genitourinary malignancies. 2-deoxy-2-[18F]fluoro-d-glucose (18F-FDG) can play an important role in the evaluation of patients with RCC and UC. In addition to the clinical utility of 18F-FDG PET to evaluate for metastatic RCC or UC, the shift in molecular imaging to focus on specific ligand-receptor interactions should provide novel diagnostic and therapeutic opportunities in genitourinary malignancies. In combination with the rise of artificial intelligence, our ability to derive imaging biomarkers that are associated with treatment selection, response assessment, and overall patient prognostication will only improve.

Clear Cell Renal Cell Carcinoma: A Test Bench for Investigating Tumor Complexity

Clear cell renal cell carcinoma (CCRCC), by far the most common renal cancer subtype, is an aggressive tumor variant, serving in recent years as a prolific test bench in cancer research [...].

Technologic advances in robot-assisted nephron sparing surgery: a narrative review

BACKGROUND AND OBJECTIVE

Nephron sparing surgery (NSS) is the preferred management for clinical stage T1 (cT1) renal masses. In recent years, indications have expanded to larger and more complex renal tumors. In an effort to provide optimal patient outcomes, urologists strive to achieve the pentafecta when performing partial nephrectomy. This has led to the continuous technologic advancement and technique refinement including the use of augmented reality, ultrasound techniques, changes in surgical approach and reconstruction, uses of novel fluorescence marker guided imaging, and implementation of early recovery after surgery (ERAS) protocols. The aim of this narrative review is to provide an overview of the recent advances in pre-, intra-, and post-operative management and approaches to managing patients with renal masses undergoing NSS.

METHODS

We performed a non-systematic literature search of PubMed and MEDLINE for the most relevant articles pertaining to the outlined topics from 2010 to 2022 without limitation on study design. We included only full-text English articles published in peer-reviewed journals.

KEY CONTENT AND FINDINGS

Partial nephrectomy is currently prioritized for cT1a renal masses; however, indications have been expanding due to a greater understanding of anatomy and technologic advances. Recent studies have demonstrated that improvements in imaging techniques utilizing cross-sectional imaging with three-dimensional (3D) reconstruction, use of color doppler intraoperative ultrasound, and newer studies emerging using contrast enhanced ultrasound play important roles in certain subsets of patients. While indocyanine green administration is commonly used, novel fluorescence-guided imaging including folate receptor-targeting fluorescence molecules are being investigated to better delineate tumor-parenchyma margins. Augmented reality has a developing role in patient and surgical trainee education. While pre-and intra-operative imaging have shown to be promising, near infrared guided segmental and sub-segmental vessel clamping has yet to show significant benefit in patient outcomes. Studies regarding reconstructive techniques and replacement of reconstruction with sealing agents have a promising future. Finally, ERAS protocols have allowed earlier discharge of patients without increasing complications while improving cost burden.

CONCLUSIONS

Advances in NSS have ranged from pre-operative imaging techniques to ERAS protocols Further prospective investigations are required to determine the impact of novel imaging, in-vivo fluorescence biomarker use, and reconstructive techniques on achieving the pentafecta of NSS.

Incremental value of radiomics with machine learning to the existing prognostic models for predicting outcome in renal cell carcinoma

Purpose

To systematically evaluate the potential of radiomics coupled with machine-learning algorithms to improve the predictive power for overall survival (OS) of renal cell carcinoma (RCC).

Methods

A total of 689 RCC patients (281 in the training cohort, 225 in the validation cohort 1 and 183 in the validation cohort 2) who underwent preoperative contrast-enhanced CT and surgical treatment were recruited from three independent databases and one institution. 851 radiomics features were screened using machine-learning algorithm, including Random Forest and Lasso-COX Regression, to establish radiomics signature. The clinical and radiomics nomogram were built by multivariate COX regression. The models were further assessed by Time-dependent receiver operator characteristic, concordance index, calibration curve, clinical impact curve and decision curve analysis.

Result

The radiomics signature comprised 11 prognosis-related features and was significantly correlated with OS in the training and two validation cohorts (Hazard Ratios: 2.718 (2.246,3.291)). Based on radiomics signature, WHOISUP, SSIGN, TNM Stage and clinical score, the radiomics nomogram has been developed. Compared with the existing prognostic models, the AUCs of 5 years OS prediction of the radiomics nomogram were superior to the TNM, WHOISUP and SSIGN model in the training cohort (0.841 vs 0.734, 0.707, 0.644) and validation cohort2 (0.917 vs 0.707, 0.773, 0.771). Stratification analysis suggested that the sensitivity of some drugs and pathways in cancer were observed different for RCC patients with high-and low-radiomics scores.

Conclusion

This study showed the application of contrast-enhanced CT-based radiomics in RCC patients, creating novel radiomics nomogram that could be used to predict OS. Radiomics provided incremental prognostic value to the existing models and significantly improved the predictive power. The radiomics nomogram might be helpful for clinicians to evaluate the benefit of surgery or adjuvant therapy and make individualized therapeutic regimens for patients with renal cell carcinoma.

Identify AGAP2 as prognostic biomarker in clear cell renal cell carcinoma based on bioinformatics and IHC staining

Background

Arf GTPase-activating proteins are aberrantly expressed in a variety of tumors, but their role in clear cell renal cell carcinoma (ccRCC) was unclear. Exploring the biological role of Arf GAP with GTP binding protein like domain, Ankyrin repeat and PH domain 2 (AGAP2) in ccRCC could improve our understanding on the aggressiveness and immune relevance of ccRCC.

Methods

The expression of AGAP2 was analyzed based on the Cancer Genome Atlas (TCGA) database and verified in ccRCC samples using immunohistochemistry. The association between AGAP2 and clinical cancer stages was explored by TCGA dataset and UALCAN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to analyze the biological functions of AGAP2-related genes. Moreover, the relationship between AGAP2 and immune cell infiltration was investigated with TIME and TCGA dataset.

Results

Compared to normal tissues, AGAP2 was upregulated in ccRCC tissues. Higher expression of AGAP2 was associated with clinical cancer stages, TNM stages, pathologic stages, and status. Prognostic analysis on AGAP2 showed that AGAP2 overexpression was associated with KIRC overall survival (OS) reduction (P = 0.019). However, higher expression of AGAP2 may improve the OS of CESC (P = 0.002), THYM (P = 0.006) and UCEC (P = 0.049). GO and KEGG analysis showed that AGAP2-related genes was related to T cell activation, immune activity and PD-L1 expression and PD-1 checkpoint pathway. Furthermore, our study showed that AGAP2 were significantly associated with T cells, Cytotoxic cells, Treg, Th1 cells, CD8 T cells, T helper cells. And AGAP2 expression level affected the abundance of immune cells infiltration. The infiltrating level of immune cells was different between the AGAP2 high-expression and low-expression groups.

Conclusion

The expression of AGAP2 in ccRCC was higher than that in normal kidney tissues. It was significantly associated with clinical stage, poor prognosis, and immune cell infiltration. Therefore, AGAP2 may become an important component for ccRCC patients who receive precision cancer therapy and may be a promising prognostic biomarker.