Update on Radiogenomics of Clear Cell Renal Cell Carcinoma

CT-Based Radiogenomics of P4HA3 Expression in Clear Cell Renal Cell Carcinoma

RATIONALE AND OBJECTIVES

The sequencing of the renal cell carcinoma (RCC) genome identified several mutations with prognostic significance. Genomic analysis, collected in The Cancer Genome Atlas Research Network, revealed several clear cell renal cell carcinoma (ccRCC) gene mutations and gene expressions. Radiogenomics is a new branch of diagnostic imaging based on the association between imaging phenotypes and genomics of diseases. P4HA3 expression has recently been shown to correlate with increased aggressiveness of ccRCC, with poor prognosis, proliferation, migration, invasion, and metastases, suggesting P4HA3 as a prognostic marker and therapeutic target in ccRCC. The aim of this study is to investigate the computed tomography (CT) imaging phenotype of P4HA3 expression in ccRCC patients.

MATERIALS AND METHODS

In this retrospective study we enrolled 196 ccRCC patients divided into two groups: ccRCC patients with P4HA3 expression (n = 13) and ccRCC patients without P4HA3 expression (n = 183). Several imaging features were evaluated on preoperative CT scan. The statistical significance threshold was set at P < .05.

RESULTS

A statistically significant association was found with larger primary tumor size (P = .033), tumor infiltration (P = .023), ill-defined tumor margins (P = .025), and advanced tumor stage American Joint Committee of Cancer (P = .014).

CONCLUSION

This study demonstrates CT imaging features associated with P4HA3 expression in ccRCC. These results could contribute to better understand P4HA3 expression with a noninvasive approach and could be applied to the development of targeted therapies.

Radiogenomic Features of GIMAP Family Genes in Clear Cell Renal Cell Carcinoma: An Observational Study on CT Images

GTPases of immunity-associated proteins (GIMAP) genes include seven functional genes and a pseudogene. Most of the GIMAPs have a role in the maintenance and development of lymphocytes. GIMAPs could inhibit the development of tumors by increasing the amount and antitumor activity of infiltrating immunocytes. Knowledge of key factors that affect the tumor immune microenvironment for predicting the efficacy of immunotherapy and establishing new targets in ccRCC is of great importance. A computed tomography (CT)-based radiogenomic approach was used to detect the imaging phenotypic features of GIMAP family gene expression in ccRCC. In this retrospective study we enrolled 193 ccRCC patients divided into two groups: ccRCC patients with GIMAP expression (n = 52) and ccRCC patients without GIMAP expression (n = 141). Several imaging features were evaluated on preoperative CT scan. A statistically significant correlation was found with absence of endophytic growth pattern (p = 0.049), tumor infiltration (p = 0.005), advanced age (p = 0.018), and high Fuhrman grade (p = 0.024). This study demonstrates CT imaging features of GIMAP expression in ccRCC. These results could allow the collection of data on GIMAP expression through a CT-approach and could be used for the development of a targeted therapy.

Relationship between body composition and PBRM1 mutations in clear cell renal cell carcinoma: a propensity score matching analysis

OBJECTIVE

This study aimed to examine the relationship between body muscle and adipose tissue composition in clear cell renal cell carcinoma patients with PBRM1 gene mutation.

METHODS

Cancer Genome Atlas Kidney clear cell renal cell carcinoma and Clinical Proteomic Tumor Analysis Consortium clear cell renal cell carcinoma collections were retrieved from the Cancer Imaging Archive. A total of 291 clear cell renal cell carcinoma patients were included in the study retrospectively. Patients' characteristics were obtained from Cancer Imaging Archive. Body composition was assessed with abdominal computed tomography using the automated artificial intelligence software (AID-U™, iAID Inc., Seoul, Korea). Body composition parameters of the patients were calculated. To investigate the net effect of body composition, the propensity score matching procedure was applied over age, gender, and T-stage parameters.

RESULTS

 Of the patients, 184 were males and 107 were females. Mutations in the PBRM1 gene were detected in 77 of the patients. While there was no difference in adipose tissue areas between the PBRM1 mutation group and those without PBRM1 mutation, statistically significant differences were found in normal attenuated muscle area parameters.

CONCLUSION

 This study shows that there was no difference between adipose tissue areas in patients with PBMR1 mutation, but normal attenuated muscle area was found to be higher in PBRM1 patients.

An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

The radiogenomics analysis can provide the connections between genomics and radiomics, which can infer the genomic features of tumors from their radiogenomic associations through the low-cost and non-invasiveness screening ultrasonic images. Although there are a number of pioneer approaches exploring the connections between genomic aberrations and ultrasonic features, these studies mainly focus on the relationship between ultrasonic features and only the most popular cancer genes, confronting two difficulties: missing many-to-many relationships as omics-to-omics view, and confounding group-specific associations with whole sample associations. To overcome the difficulty of omics-to-omics view and the issue of tumor heterogeneity, we propose an omics-to-omics joint knowledge association subtensor model. Specifically, the subtensor factorization framework can successfully discover the joint cross-modal module via an omics-to-omics view, while the sparse weight sample indication strategy can mine sample subgroups from the multi-omic data with tumor heterogeneity. The experimental evaluation result shows the jointness of the discovered modules across omics, their association with tumorigenesis contribution, and their relation for cancer related functions. In summary, our proposed omics-to-omics joint knowledge association subtensor model can serve as an efficient tool for radiogenomic knowledge associations, promoting the cross-modal knowledge graph construction of in explainable artificial intelligence cancer diagnosis.

The Emerging Role of PET/CT with PSMA-Targeting Radiopharmaceuticals in Clear Cell Renal Cancer: An Updated Systematic Review

BACKGROUND

Recent articles proposed the employment of positron emission tomography/computed tomography (PET/CT) with prostate-specific membrane antigen (PSMA)-targeting radiopharmaceuticals in clear cell renal cell carcinoma (ccRCC).

METHODS

The authors performed a comprehensive literature search of studies on the performance of PET/CT with PSMA-targeting radiopharmaceuticals in ccRCC. Original articles concerning this imaging examination were included in newly diagnosed ccRCC patients and ccRCC patients with disease recurrence.

RESULTS

A total of sixteen papers concerning the diagnostic performance of PSMA-targeted PET/CT in ccRCC (331 patients) were included in this systematic review. The included articles demonstrated an excellent detection rate of PSMA-targeting PET/CT in ccRCC.

CONCLUSIONS

PSMA-targeted PET/CT seems promising in detecting ccRCC lesions as well as in discriminating the presence of aggressive phenotypes. Prospective multicentric studies are warranted to strengthen the role of PSMA-targeting PET/CT in ccRCC.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

A Reliable Prediction Model for Renal Cell Carcinoma Subtype Based on Radiomic Features from 3D Multiphase Enhanced CT Images

Background

This study aimed to develop a prediction model to distinguish renal cell carcinoma (RCC) subtypes.

Methods

The radiomic features (RFs) from 5 different computed tomography (CT) phases were used in the prediction models: noncontrast phase (NCP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP), and all-phase (ALL-P).

Results

For the ALL-P model, all of the RFs obtained from the 4 single-phase images were combined to 420 RFs. The ALL-P model performed the best of all models, with an accuracy of 0.80; the sensitivity and specificity for clear cell RCC (ccRCC) were 0.85 and 0.83; those for papillary RCC (pRCC) were 0.60 and 0.91; those for chromophobe RCC (cRCC) were 0.66 and 0.91, respectively. Binary classification experiments showed for distinguishing ccRCC vs. not-ccRCC that the area under the receiver operating characteristic curve (AUC) of the ALL-P and CMP models was 0.89, but the overall sensitivity/specificity/accuracy of the ALL-P model was better. For cRCC vs. non-cRCC, the ALL-P model had the best performance.

Conclusions

A reliable prediction model for RCC subtypes was constructed. The performance of the ALL-P prediction model was the best as compared to individual single-phase models and the traditional prediction model.

Relationship between visceral adipose tissue and genetic mutations (VHL and KDM5C) in clear cell renal cell carcinoma

BACKGROUND

The sequencing of the renal cell carcinoma (RCC) genome has detected several mutations with prognostic meaning. The association between visceral adipose tissue (VAT) and clear cell renal cell carcinoma (ccRCC) is well known. The relationship among abdominal adipose tissue distribution and ccRCC-VHL and KDM5C genetic mutations is, to the knowledge of the authors, not known.

METHODS

In this retrospective study, we enrolled 97 Caucasian male patients divided into three groups: the control group (n = 35), the ccRCC-VHL group (n = 52) composed of ccRCC patients with VHL mutations and ccRCC-KDM5C group (n = 10) composed of ccRCC patients with KDM5C mutation. Total adipose tissue (TAT) area, VAT area and subcutaneous adipose tissue (SAT) area were measured in the groups. VAT/SAT ratio was calculated for each subject.

RESULTS

Statistically significant differences between ccRCC-KDM5C group and ccRCC-VHL group were obtained for TAT area (p < 0.05), VAT area (p < 0.05) and VAT/SAT ratio (p < 0.05); between ccRCC-VHL group and control group for TAT area (p < 0.001) and VAT area (p < 0.01); and between ccRCC-KDM5C group and control group for TAT area (p < 0.0001), VAT area (p < 0.0001) and SAT area (p < 0.01).

CONCLUSIONS

This study demonstrates for the first time an increased amount of TAT, especially VAT, in the ccRCC-VHL and ccRCC-KDM5C groups. The effect was greater for the ccRCC-KDM5C group.

Integrative Radiogenomics Approach for Risk Assessment of Post-Operative Metastasis in Pathological T1 Renal Cell Carcinoma: A Pilot Retrospective Cohort Study

Despite the increasing incidence of pathological stage T1 renal cell carcinoma (pT1 RCC), postoperative distant metastases develop in many surgically treated patients, causing death in certain cases. Therefore, this study aimed to create a radiomics model using imaging features from multiphase computed tomography (CT) to more accurately predict the postoperative metastasis of pT1 RCC and further investigate the possible link between radiomics parameters and gene expression profiles generated by whole transcriptome sequencing (WTS). Four radiomic features, including the minimum value of a histogram feature from inner regions of interest (ROIs) (INNER_Min_hist), the histogram of the energy feature from outer ROIs (OUTER_Energy_Hist), the maximum probability of gray-level co-occurrence matrix (GLCM) feature from inner ROIs (INNER_MaxProb_GLCM), and the ratio of voxels under 80 Hounsfield units (Hus) in the nephrographic phase of postcontrast CT (Under80HURatio), were detected to predict the postsurgical metastasis of patients with pathological stage T1 RCC, and the clinical outcomes of patients could be successfully stratified based on their radiomic risk scores. Furthermore, we identified heterogenous-trait-associated gene signatures correlated with these four radiomic features, which captured clinically relevant molecular pathways, tumor immune microenvironment, and potential treatment strategies. Our results of accurate surrogates using radiogenomics could lead to additional benefit from adjuvant therapy or postsurgical metastases in pT1 RCC.

Radiogenomics in renal cell carcinoma

Radiogenomics, a field of radiology investigating the association between the imaging features of a disease and its gene expression pattern, has expanded considerably in the last few years. Recent advances in whole-genome sequencing of clear cell renal cell carcinoma (ccRCC) and the identification of mutations with prognostic significance have led to increased interest in the relationship between imaging and genomic data. ccRCC is particularly suitable for radiogenomic analysis as the relative paucity of mutated genes allows for more straightforward genomic-imaging associations. The ultimate aim of radiogenomics of ccRCC is to retrieve additional data for accurate diagnosis, prognostic stratification, and optimization of therapy. In this review article, we will present the state-of-the-art of radiogenomics of ccRCC, and after briefly reviewing updates in genomics, we will discuss imaging-genomic associations for diagnosis and staging, prognosis, and for assessment of optimal therapy in ccRCC.

Radiogenomics of Clear Cell Renal Cell Carcinoma: Associations Between mRNA-Based Subtyping and CT Imaging Features

PURPOSE

To investigate associations between clear-cell renal cell carcinoma mRNA-based subtyping and CT features.

MATERIALS AND METHODS

The CT data from 177 patients generated with The Cancer Imaging Archive were reviewed. The correlation was analyzed using chi-square test and univariate regression analysis.

RESULTS

Identified were 124 (53.2%) m1, 67 (28.8%) m2, 17 (7.3%) m3, and 14 (8.7%) m4 subtypes. m1-subtype rates were significantly higher in well-defined margin lesions (p = 0.041). m3-subtype rates were significantly higher in ill-defined margin lesions (p = 0.012), in collecting system invasion lesions (p = 0.028) and collecting system invasion lesions (p = 0.026).On univariate logistic regression analysis, tumor margin (well-defined margin vs ill-defined margin, OR: 2.104; p = 0.041; 95% CI: 1.024-4.322) was associated with m1-subtype. Tumor margin (well-defined margin vs ill-defined margin, OR: 2.104; p = 0.012; 95% CI: 0.212-0.834) and collecting system invasion (yes vs no, OR: 0.421; p = 0.028; 95% CI: 0.212-0.834) and renal vein invasion (yes vs no, OR: 2.164; p = 0.026; 95% CI: 1.090-4.294) were associated with m3-subtype. There was no significant difference between mRNA-based subtyping (m2 vs other; m4 vs other) and the CT features.

CONCLUSIONS

This preliminary radiogenomics analysis of clear-cell renal cell carcinoma revealed associations between CT features and mRNA-based subtyping which warrant further investigation and validation.

Renal cell carcinoma: predicting RUNX3 methylation level and its consequences on survival with CT features

PURPOSE

To investigate associations between CT imaging features, RUNX3 methylation level, and survival in clear cell renal cell carcinoma (ccRCC).

MATERIALS AND METHODS

Patients were divided into high RUNX3 methylation and low RUNX3 methylation groups according to RUNX3 methylation levels (the threshold was identified by using X-tile). The CT scanning data from 106 ccRCC patients were retrospectively analyzed. The relationship between RUNX3 methylation level and overall survivals was evaluated using the Kaplan-Meyer analysis and Cox regression analysis (univariate and multivariate). The relationship between RUNX3 methylation level and CT features was evaluated using chi-square test and logistic regression analysis (univariate and multivariate).

RESULTS

β value cutoff of 0.53 to distinguish high methylation (N = 44) from low methylation tumors (N = 62). Patients with lower levels of methylation had longer median overall survival (49.3 vs. 28.4) months (low vs. high, adjusted hazard ratio [HR] 4.933, 95% CI 2.054-11.852, p < 0.001). On univariate logistic regression analysis, four risk factors (margin, side, long diameter, and intratumoral vascularity) were associated with RUNX3 methylation level (all p < 0.05). Multivariate logistic regression analysis found that three risk factors (side: left vs. right, odds ratio [OR] 2.696; p = 0.024; 95% CI 1.138-6.386; margin: ill-defined vs. well-defined, OR 2.685; p = 0.038; 95% CI 1.057-6.820; and intratumoral vascularity: yes vs. no, OR 3.286; p = 0.008; 95% CI 1.367-7.898) were significant independent predictors of high methylation tumors. This model had an area under the receiver operating characteristic curve (AUC) of 0.725 (95% CI 0.623-0.827).

CONCLUSIONS

Higher levels of RUNX3 methylation are associated with shorter survival in ccRCC patients. And presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.

KEY POINTS

• RUNX3 methylation level is negatively associated with overall survival in ccRCC patients. • Presence of intratumoral vascularity, ill-defined margin, and left side tumor were significant independent predictors of high methylation level of RUNX3 gene.

Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status

OBJECTIVE

The purpose of this study is to evaluate the potential value of machine learning (ML)-based high-dimensional quantitative CT texture analysis in predicting the mutation status of the gene encoding the protein polybromo-1 (PBRM1) in patients with clear cell renal cell carcinoma (RCC).

MATERIALS AND METHODS

In this retrospective study, 45 patients with clear cell RCC (29 without the PBRM1 mutation and 16 with the PBRM1 mutation) were identified in The Cancer Genome Atlas-Kidney Renal Clear Cell Carcinoma database. To create stable ML models and balanced classes, the data were augmented to a total of 161 labeled segmentations (87 without the PBRM1 mutation and 74 with the PBRM1 mutation) by obtaining three to five different samples per patient. Texture features were extracted from corticomedullary phase contrast-enhanced CT images with the use of an open-source software package for the extraction of radiomic data from medical images. Reproducibility analysis (intraclass correlation) was performed by two radiologists. Attribute selection and model optimization were done using a wrapper-based classifier-specific algorithm with nested cross-validation. ML classifiers were an artificial neural network (ANN) algorithm and a random forest (RF) algorithm. The models were validated using 10-fold cross-validation. The reference standard was the PBRM1 mutation status. The main performance metric was the AUC value.

RESULTS

Of 828 extracted texture features, 759 had excellent reproducibility. Using 10 selected features, the ANN algorithm correctly classified 88.2% (142 of 161) of the clear cell RCCs in terms of PBRM1 mutation status (AUC value, 0.925). Using five selected features, the RF algorithm correctly classified 95.0% (153 of 161) of the clear cell RCCs (AUC value, 0.987). Overall, the RF algorithm performed better than the ANN algorithm (z score = -2.677; p = 0.007).

CONCLUSION

ML-based high-dimensional quantitative CT texture analysis might be a feasible and potential method for predicting PBRM1 mutation status in patients with clear cell RCC.