Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

Development and validation of a multicenter Cox regression model to predict all-cause mortality in patients with renal masses suspicious for renal cancer

OBJECTIVE

Life expectancy models are useful tools to support clinical decision-making. Prior models have not been used widely in clinical practice for patients with renal masses. We sought to develop and validate a model to predict life expectancy following the detection of a localized renal mass suspicious for renal cell carcinoma.

MATERIALS AND METHODS

Using retrospective data from 2 large centers, we identified patients diagnosed with clinically localized renal parenchymal masses from 1998 to 2018. After 2:1 random sampling into a derivation and validation cohort stratified by site, we used age, sex, log-transformed tumor size, simplified cardiovascular index and planned treatment to fit a Cox regression model to predict all-cause mortality from the time of diagnosis. The model's discrimination was evaluated using a C-statistic, and calibration was evaluated visually at 1, 5, and 10 years.

RESULTS

We identified 2,667 patients (1,386 at Corewell Health and 1,281 at Johns Hopkins) with renal masses. Of these, 420 (16%) died with a median follow-up of 5.2 years (interquartile range 2.2-8.3). Statistically significant predictors in the multivariable Cox regression model were age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.03-1.05); male sex (HR 1.40; 95% CI 1.08-1.81); log-transformed tumor size (HR 1.71; 95% CI 1.30-2.24); cardiovascular index (HR 1.48; 95% CI 1.32-1.67), and planned treatment (HR: 0.10, 95% CI: 0.06-0.18 for kidney-sparing intervention and HR: 0.20, 95% CI: 0.11-0.35 for radical nephrectomy vs. no intervention). The model achieved a C-statistic of 0.74 in the derivation cohort and 0.73 in the validation cohort. The model was well-calibrated at 1, 5, and 10 years of follow-up.

CONCLUSIONS

For patients with localized renal masses, accurate determination of life expectancy is essential for decision-making regarding intervention vs. active surveillance as a primary treatment modality. We have made available a simple tool for this purpose.

ImmunoPET/CT imaging of clear cell renal cell carcinoma with [18F]RCCB6: a first-in-human study

PURPOSE

The cluster of differentiation (CD70) is a potential biomarker of clear cell renal cell carcinoma (ccRCC). This study aims to develop CD70-targeted immuno-positron emission tomography/computed tomography (immunoPET/CT) imaging tracers and explore the diagnostic value in preclinical studies and the potential value in detecting metastases in ccRCC patients.

METHODS

Four novel CD70-specific single-domain antibodies (sdAbs) were produced and labelled with 18F by the aluminium fluoride restrained complexing agent (AlF-RESCA) method to develop radiotracers. The visualisation properties of the tracers were evaluated in a subcutaneous ccRCC patient-derived xenograft (PDX) model. In a registered prospective clinical trial (NCT06148220), six patients with pathologically confirmed RCC were included and underwent immunoPET/CT examination exploiting one of the developed tracers (i.e., [18F]RCCB6).

RESULTS

We engineered four sdAbs (His-tagged RCCB3 and RCCB6, His-tag-free RB3 and RB6) specifically targeting recombinant human CD70 without cross-reactivity to murine CD70. ImmunoPET/CT imaging with [18F]RCCB3 and [18F]RCCB6 demonstrated a high tumour-to-background ratio in a subcutaneous ccRCC PDX model, with the latter showing better diagnostic potential supported by higher tumour uptake and lower bone accumulation. In comparison, [18F]RB6, developed by sequence optimisation, has significantly lower kidney accumulation than that of [18F]RCCB6. In a pilot translational study, [18F]RCCB6 immunoPET/CT displayed ccRCC metastases in multiple patients and demonstrated improved imaging contrast and diagnostic value than 18F-FDG PET/CT in a patient with ccRCC.

CONCLUSION

The work successfully developed a series of CD70-targeted immunoPET/CT imaging tracers. Of them, [18F]RCCB6 clearly and specifically identified inoculated ccRCCs in preclinical studies. Clinical translation of [18F]RCCB6 suggests potential for identifying recurrence and/or metastasis in ccRCC patients.

Predominantly multiple focal non-cystic renal lesions: an imaging approach

Multiple non-cystic renal lesions are occasionally discovered during imaging for various reasons and poses a diagnostic challenge to the practicing radiologist. These lesions may appear as a primary or dominant imaging finding or may be an additional abnormality in the setting of multiorgan involvement. Awareness of the imaging appearance of the various entities presenting as renal lesions integrated with associated extrarenal imaging findings along with clinical information is crucial for a proper diagnostic approach and patient work-up. This review summarizes the most relevant causes of infectious, inflammatory, vascular, and neoplastic disorders presenting as predominantly multiple focal non-cystic lesions.

Evaluation of PET/CT imaging with [89Zr]Zr-DFO-girentuximab: a phase 1 clinical study in Japanese patients with renal cell carcinoma (Zirdac-JP)

BACKGROUND

PET/CT imaging with Zirconium-89 labeled [89Zr]Zr-DFO-girentuximab, which targets tumor antigen CAIX, may aid in the differentiation and characterization of clear cell renal cell carcinomas (RCC) and other renal and extrarenal lesions, and has been studied in European and American cohorts. We report results from a phase I study that evaluated the safety profile, biodistribution, and dosimetry of [89Zr]Zr-DFO-girentuximab in Japanese patients with suspected RCC.

METHODS

Eligible adult patients received 37 MBq (± 10%; 10 mg mass dose) of intravenous [89Zr]Zr-DFO-girentuximab. Safety and tolerability profile was assessed based on adverse events, concomitant medications, physical examination, vital signs, hematology, serum chemistry, urinalysis, human anti-chimeric antibody measurement, and 12-lead electrocardiograms at predefined intervals. Biodistribution and normal organ and tumor dosimetry were evaluated with PET/CT images acquired at 0.5, 4, 24, 72 h and Day 5 ± 2 d after administration.

RESULTS

[89Zr]Zr-DFO-girentuximab was administered in six patients as per protocol. No treatment-emergent adverse events were reported. Dosimetry analysis showed that radioactivity was widely distributed in the body, and that the absorbed dose in healthy organs was highest in the liver (mean ± standard deviation) (1.365 ± 0.245 mGy/MBq), kidney (1.126 ± 0.190 mGy/MBq), heart wall (1.096 ± 0.232 mGy/MBq), and spleen (1.072 ± 0.466 mGy/MBq). The mean effective dose, adjusted by the radioactive dose administered, was 0.470 mSv/MBq. The radiation dose was highly accumulated in the targeted tumor, while any abnormal accumulation in other organs was not reported.

CONCLUSIONS

This study demonstrates that [89Zr]Zr-DFO-girentuximab administered to Japanese patients with suspected RCC has a favorable safety profile and is well tolerated and has a similar dosimetry profile to previously studied populations.

Clinical T1/2 renal cell carcinoma: multiparametric dynamic contrast-enhanced MRI features-based model for the prediction of individual adverse pathology

BACKGROUND

The detection of renal cell carcinoma (RCC) has been rising due to the enhanced utilization of cross-sectional imaging and incidentally discovered lesions with adverse pathology demonstrate potential for metastasis. The purpose of our study was to determine the clinical and multiparametric dynamic contrast-enhanced magnetic resonance imaging (CEMRI) associated independent predictors of adverse pathology for cT1/2 RCC and develop the predictive model.

METHODS

We recruited 105 cT1/2 RCC patients between 2018 and 2022, all of whom underwent preoperative CEMRI and had complete clinicopathological data. Adverse pathology was defined as RCC patients with nuclear grade III-IV; pT3a upstage; type II papillary RCC, collecting duct or renal medullary carcinoma, unclassified RCC; sarcomatoid/rhabdoid features. The qualitative and quantitative CEMRI parameters were independently reviewed by two radiologists. Univariate and multivariate binary logistic regression analyses were utilized to determine the independent predictors of adverse pathology for cT1/2 RCC and construct the predictive model. The receiver operating characteristic (ROC) curve, confusion matrix, calibration plot, and decision curve analysis (DCA) were conducted to compare the diagnostic performance of different predictive models. The individual risk scores and linear predicted probabilities were calculated for risk stratification, and the Kaplan-Meier curve and log-rank tests were used for survival analysis.

RESULTS

Overall, 45 patients were pathologically confirmed as RCC with adverse pathology. Clinical characteristics, including gender, and CEMRI parameters, including RENAL score, tumor margin irregularity, necrosis, and tumor apparent diffusion coefficient (ADC) value were identified as independent predictors of adverse pathology for cT1/2 RCC. The clinical-CEMRI predictive model yielded an area under the curve (AUC) of the ROC curve of 0.907, which outperformed the clinical model or CEMRI signature model alone. Good calibration, better clinical usefulness, excellent risk stratification ability of adverse pathology and prognosis were also achieved for the clinical-CEMRI predictive model.

CONCLUSIONS

The proposed clinical-CEMRI predictive model offers the potential for preoperative prediction of adverse pathology for cT1/2 RCC. With the ability to forecast adverse pathology, the predictive model could significantly benefit patients and clinicians alike by providing enhanced guidance for treatment planning and decision-making.

Diagnostic Value of Clear Cell Likelihood Score v1.0 and v2.0 for Common Subtypes of Small Renal Masses: A Multicenter Comparative Study

BACKGROUND

Clear cell likelihood score (ccLS) is reliable for diagnosing small renal masses (SRMs). However, the diagnostic value of Clear cell likelihood score version 1.0 (ccLS v1.0) and v2.0 for common subtypes of SRMs might be a potential score extension.

PURPOSE

To compare the diagnostic performance and interobserver agreement of ccLS v1.0 and v2.0 for characterizing five common subtypes of SRMs.

STUDY TYPE

Retrospective.

POPULATION

797 patients (563 males, 234 females; mean age, 53 ± 12 years) with 867 histologically proven renal masses.

FIELD STRENGTH/SEQUENCES

3.0 and 1.5 T/T2 weighted imaging, T1 weighted imaging, diffusion-weighted imaging, a dual-echo chemical shift (in- and opposed-phase) T1 weighted imaging, multiphase dynamic contrast-enhanced imaging.

ASSESSMENT

Six abdominal radiologists were trained in the ccLS algorithm and independently scored each SRM using ccLS v1.0 and v2.0, respectively. All SRMs had definite pathological results. The pooled area under curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate the diagnostic performance of ccLS v1.0 and v2.0 for characterizing common subtypes of SRMs. The average κ values were calculated to evaluate the interobserver agreement of the two scoring versions.

STATISTICAL TESTS

Random-effects logistic regression; Receiver operating characteristic analysis; DeLong test; Weighted Kappa test; Z test. The statistical significance level was P < 0.05.

RESULTS

The pooled AUCs of clear cell likelihood score version 2.0 (ccLS v2.0) were statistically superior to those of ccLS v1.0 for diagnosing clear cell renal cell carcinoma (ccRCC) (0.907 vs. 0.851), papillary renal cell carcinoma (pRCC) (0.926 vs. 0.888), renal oncocytoma (RO) (0.745 vs. 0.679), and angiomyolipoma without visible fat (AMLwvf) (0.826 vs. 0.766). Interobserver agreement for SRMs between ccLS v1.0 and v2.0 is comparable and was not statistically significant (P = 0.993).

CONCLUSION

The diagnostic performance of ccLS v2.0 surpasses that of ccLS v1.0 for characterizing ccRCC, pRCC, RO, and AMLwvf. Especially, the standardized algorithm has optimal performance for ccRCC and pRCC. ccLS has potential as a supportive clinical tool.

EVIDENCE LEVEL

4.

TECHNICAL EFFICACY

Stage 2.

Diagnostic Biopsy for Small Renal Tumours: A Survey of Current European Practice

Background and objective

Renal tumour biopsy (RTB) can help in risk stratification of renal tumours with implications for management, but its utilisation varies. Our objective was to report current practice patterns, experiences, and perceptions of RTB and research gaps regarding RTB for small renal masses (SRMs).

Methods

Two web-based surveys, one for health care providers (HCPs) and one for patients, were distributed via the European Association of Urology Young Academic Urologist Renal Cancer Working Group and the European Society of Residents in Urology in January 2023.

Key findings and limitations

The HCP survey received 210 responses (response rate 51%) and the patient survey 54 responses (response rate 59%). A minority of HCPs offer RTB to >50% of patients (14%), while 48% offer it in <10% of cases. Most HCPs reported that RTB influences (61.5%) or sometimes influences (37.1%) management decisions. Patients were more likely to favour active treatment if RTB showed high-grade cancer and less likely to favour active treatment for benign histology. HCPs identified situations in which they would not favour RTB, such as cystic tumours and challenging anatomic locations. RTB availability (67%) and concerns about delays to treatment (43%) were barriers to offering RTB. Priority research gaps include a trial demonstrating that RTB leads to better clinical outcomes, and better evidence that benign/indolent tumours do not require active treatment.

Conclusions and clinical implications

Utilisation of RTB for SRMs in Europe is low, even though both HCPs and patients reported that RTB results can affect disease management. Improving timely access to RTB and generating evidence on outcomes associated with RTB use are priorities for the kidney cancer community.

Patient summary

A biopsy of a kidney mass can help patients and doctors make decisions on treatment, but our survey found that many patients in Europe are not offered this option. Better access to biopsy services is needed, as well as more research on what happens to patients after biopsy.

Multiple and hereditary renal tumors: a review for radiologists

80% of renal carcinomas (RC) are diagnosed incidentally by imaging. 2-4% of "sporadic" multifocality and 5-8% of hereditary syndromes are accepted, probably with underestimation. Multifocality, young age, familiar history, syndromic data, and certain histologies lead to suspicion of hereditary syndrome. Each tumor must be studied individually, with a multidisciplinary evaluation of the patient. Nephron-sparing therapeutic strategies and a radioprotective diagnostic approach are recommended. Relevant data for the radiologist in major RC hereditary syndromes are presented: von-Hippel-Lindau, Chromosome-3 translocation, BRCA-associated protein-1 mutation, RC associated with succinate dehydrogenase deficiency, PTEN, hereditary papillary RC, Papillary thyroid cancer- Papillary RC, Hereditary leiomyomatosis and RC, Birt-Hogg-Dubé, Tuberous sclerosis complex, Lynch, Xp11.2 translocation/TFE3 fusion, Sickle cell trait, DICER1 mutation, Hereditary hyperparathyroidism and jaw tumor, as well as the main syndromes of Wilms tumor predisposition. The concept of "non-hereditary" familial RC and other malignant and benign entities that can present as multiple renal lesions are discussed.

Emerging Trends in AI and Radiomics for Bladder, Kidney, and Prostate Cancer: A Critical Review

This comprehensive review critically examines the transformative impact of artificial intelligence (AI) and radiomics in the diagnosis, prognosis, and management of bladder, kidney, and prostate cancers. These cutting-edge technologies are revolutionizing the landscape of cancer care, enhancing both precision and personalization in medical treatments. Our review provides an in-depth analysis of the latest advancements in AI and radiomics, with a specific focus on their roles in urological oncology. We discuss how AI and radiomics have notably improved the accuracy of diagnosis and staging in bladder cancer, especially through advanced imaging techniques like multiparametric MRI (mpMRI) and CT scans. These tools are pivotal in assessing muscle invasiveness and pathological grades, critical elements in formulating treatment plans. In the realm of kidney cancer, AI and radiomics aid in distinguishing between renal cell carcinoma (RCC) subtypes and grades. The integration of radiogenomics offers a comprehensive view of disease biology, leading to tailored therapeutic approaches. Prostate cancer diagnosis and management have also seen substantial benefits from these technologies. AI-enhanced MRI has significantly improved tumor detection and localization, thereby aiding in more effective treatment planning. The review also addresses the challenges in integrating AI and radiomics into clinical practice, such as the need for standardization, ensuring data quality, and overcoming the "black box" nature of AI. We emphasize the importance of multicentric collaborations and extensive studies to enhance the applicability and generalizability of these technologies in diverse clinical settings. In conclusion, AI and radiomics represent a major paradigm shift in oncology, offering more precise, personalized, and patient-centric approaches to cancer care. While their potential to improve diagnostic accuracy, patient outcomes, and our understanding of cancer biology is profound, challenges in clinical integration and application persist. We advocate for continued research and development in AI and radiomics, underscoring the need to address existing limitations to fully leverage their capabilities in the field of oncology.

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.

Convolutional neural networks for the differentiation between benign and malignant renal tumors with a multicenter international computed tomography dataset

OBJECTIVES

To use convolutional neural networks (CNNs) for the differentiation between benign and malignant renal tumors using contrast-enhanced CT images of a multi-institutional, multi-vendor, and multicenter CT dataset.

METHODS

A total of 264 histologically confirmed renal tumors were included, from US and Swedish centers. Images were augmented and divided randomly 70%:30% for algorithm training and testing. Three CNNs (InceptionV3, Inception-ResNetV2, VGG-16) were pretrained with transfer learning and fine-tuned with our dataset to distinguish between malignant and benign tumors. The ensemble consensus decision of the three networks was also recorded. Performance of each network was assessed with receiver operating characteristics (ROC) curves and their area under the curve (AUC-ROC). Saliency maps were created to demonstrate the attention of the highest performing CNN.

RESULTS

Inception-ResNetV2 achieved the highest AUC of 0.918 (95% CI 0.873-0.963), whereas VGG-16 achieved an AUC of 0.813 (95% CI 0.752-0.874). InceptionV3 and ensemble achieved the same performance with an AUC of 0.894 (95% CI 0.844-0.943). Saliency maps indicated that Inception-ResNetV2 decisions are based on the characteristics of the tumor while in most tumors considering the characteristics of the interface between the tumor and the surrounding renal parenchyma.

CONCLUSION

Deep learning based on a diverse multicenter international dataset can enable accurate differentiation between benign and malignant renal tumors.

CRITICAL RELEVANCE STATEMENT

Convolutional neural networks trained on a diverse CT dataset can accurately differentiate between benign and malignant renal tumors.

KEY POINTS

• Differentiation between benign and malignant tumors based on CT is extremely challenging. • Inception-ResNetV2 trained on a diverse dataset achieved excellent differentiation between tumor types. • Deep learning can be used to distinguish between benign and malignant renal tumors.

Radiomics analysis based on single phase and different phase combinations of radiomics features from tri-phasic CT to distinguish renal oncocytoma from chromophobe renal cell carcinoma

OBJECTIVES

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Differentiation between renal epithelioid angiomyolipoma and clear cell renal cell carcinoma using clear cell likelihood score

PURPOSE

Clear cell likelihood score (ccLS) may be a reliable diagnostic method for distinguishing renal epithelioid angiomyolipoma (EAML) and clear cell renal cell carcinoma (ccRCC). In this study, we aim to explore the value of ccLS in differentiating EAML from ccRCC.

METHODS

We performed a retrospective analysis in which 27 EAML patients and 60 ccRCC patients underwent preoperative magnetic resonance imaging (MRI) at our institution. Two radiologists trained in the ccLS algorithm scored independently and the consistency of their interpretation was evaluated. The difference of the ccLS score was compared between EAML and ccRCC in the whole study cohort and two subgroups [small renal masses (SRM; ≤ 4 cm) and large renal masses (LRM; > 4 cm)].

RESULTS

In total, 87 patients (59 men, 28 women; mean age, 55±11 years) with 90 renal masses (EAML: ccRCC = 1: 2) were identified. The interobserver agreement of two radiologists for the ccLS system to differentiate EAML from ccRCC was good (k = 0.71). The ccLS score in the EAML group and the ccRCC group ranged from 1 to 5 (73.3% in scores 1-2) and 2 to 5 (76.7% in scores 4-5), respectively, with statistically significant differences (P < 0.001). With the threshold value of 2, ccLS can distinguish EAML from ccRCC with the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 87.8%, 95.0%, 73.3%, 87.7%, and 88.0%, respectively. The AUC (area under the curve) was 0.913. And the distribution of the ccLS score between the two diseases was not affected by tumor size (P = 0.780).

CONCLUSION

The ccLS can distinguish EAML from ccRCC with high accuracy and efficiency.

PSMA-Expression Is Highly Associated with Histological Subtypes of Renal Cell Carcinoma: Potential Implications for Theranostic Approaches

In renal cell carcinoma (RCC), accurate imaging methods are required for treatment planning and response assessment to therapy. In addition, there is an urgent need for new therapeutic options, especially in metastatic RCC. One way to combine diagnostics and therapy in a so-called theranostic approach is the use of radioligands directed against surface antigens. For instance, radioligands against prostate-specific membrane antigen (PSMA) have already been successfully used for diagnosis and radionuclide therapy of metastatic prostate cancer. Recent studies have demonstrated that PSMA is expressed not only in prostate cancer but also in the neovasculature of several solid tumors, which has raised hopes to use PSMA-guided theranostic approaches in other tumor entities, too. However, data on PSMA expression in different histopathological subtypes of RCC are sparse. Because a better understanding of PSMA expression in RCC is critical to assess which patients would benefit most from theranostic approaches using PSMA-targeted ligands, we investigated the expression pattern of PSMA in different subtypes of RCC on protein level. Immunohistochemical staining for PSMA was performed on formalin-fixed, paraffin-embedded archival material of major different histological subtypes of RCC (clear cell RCC (ccRCC)), papillary RCC (pRCC) and chromophobe RCC (cpRCC). The extent and intensity of PSMA staining were scored semi-quantitatively and correlated with the histological RCC subtypes. Group comparisons were calculated with the Kruskal-Wallis test. In all cases, immunoreactivity was detected only in the tumor-associated vessels and not in tumor cells. Staining intensity was the strongest in ccRCC, followed by cpRCC and pRCC. ccRCC showed the most diffuse staining pattern, followed by cpRCC and pRCC. Our results provide a rationale for PSMA-targeted theranostic approaches in ccRCC and cpRCC.

BR55 Ultrasound Molecular Imaging of Clear Cell Renal Cell Carcinoma Reflects Tumor Vascular Expression of VEGFR-2 in a Patient-Derived Xenograft Model

Standard imaging cannot reliably predict the nature of renal tumors. Among malignant renal tumors, clear cell renal cell carcinoma (ccRCC) is the most common histological subtype, in which the vascular endothelial growth factor 2 (VEGFR-2) is highly expressed in the vascular endothelium. BR55, a contrast agent for ultrasound imaging, consists of gas-core lipid microbubbles that specifically target and bind to the extracellular portion of the VEGFR-2. The specific information provided by ultrasound molecular imaging (USMI) using BR55 was compared with the vascular tumor expression of the VEGFR-2 by immunohistochemical (IHC) staining in a preclinical model of ccRCC. Patients' ccRCCs were orthotopically grafted onto Nod-Scid-Gamma (NSG) mice to generate patient-derived xenografts (PdX). Mice were divided into four groups to receive either vehicle or axitinib an amount of 2, 7.5 or 15 mg/kg twice daily. Perfusion parameters and the BR55 ultrasound contrast signal on PdX renal tumors were analyzed at D0, D1, D3, D7 and D11, and compared with IHC staining for the VEGFR-2 and CD34. Significant Pearson correlation coefficients were observed between the area under the curve (AUC) and the CD34 (0.84, p < 10-4), and between the VEGFR-2-specific signal obtained by USMI and IHC (0.72, p < 10-4). USMI with BR55 could provide instant, quantitative information on tumor VEGFR-2 expression to characterize renal masses non-invasively.

Effect of smoking, hypertension and lifestyle factors on kidney cancer - perspectives for prevention and screening programmes

Renal cell carcinoma (RCC) incidence has doubled over the past few decades. However, death rates have remained stable as the number of incidental renal mass diagnoses peaked. RCC has been recognized as a European health care issue, but to date, no screening programmes have been introduced. Well-known modifiable risk factors for RCC are smoking, obesity and hypertension. A direct association between cigarette consumption and increased RCC incidence and RCC-related death has been reported, but the underlying mechanistic pathways for this association are still unclear. Obesity is associated with an increased risk of RCC, but interestingly, improved survival outcomes have been reported in obese patients, a phenomenon known as the obesity paradox. Data on the association between other modifiable risk factors such as diet, dyslipidaemia and physical activity with RCC incidence are conflicting, and potential mechanisms underlying these associations remain to be elucidated.

The national utilization of nonoperative management for small renal masses over 10 years

BACKGROUND

Management of small renal masses often involves a nonoperative approach, but there is a paucity of information about the use and associated predictors of such approaches. This study aimed to determine the trends in and predictors of use of nonoperative management of small renal masses.

METHODS

Using data from the National Cancer Database for localized small renal masses (N0/M0, cT1a) diagnosed between 2010 and 2020, we conducted a cross-sectional study. Nonoperative management was defined as expectant management (active surveillance or watchful waiting) or focal ablation. Adjusted odds ratios (AORs) were calculated using multivariable logistic regression models.

RESULTS

Of the 156 734 patients included, 10.5% underwent expectant management, and 13.9% underwent focal ablation. Later year of diagnosis was associated with a higher likelihood of nonoperative management. In 2020, the odds of receiving expectant management and focal ablation were 90% (AOR = 1.90, 95% confidence interval [CI] = 1.71 to 2.11) and 44% (AOR = 1.44, 95% CI = 1.31 to 1.57) higher, respectively, than in 2010. Black patients had increased odds of expectant management (AOR = 1.47, 95% CI = 1.39 to 1.55) but decreased odds of focal ablation (AOR = 0.93, 95% CI = 0.88 to 0.99).

CONCLUSION

Over the decade, the use nonoperative management of small renal masses increased, with expectant management more frequently used than focal ablation among Black patients. Possible explanations include race-based differences in physicians' risk assessments and resource allocation. Adjusting for Black race in calculations for glomerular filtration rate could influence the differential uptake of these techniques through deflated glomerular filtration rate calculations. These findings highlight the need for research and policies to ensure equitable use of less invasive treatments in small renal masses.

Insights into Renal Cell Carcinoma with Novel Imaging Approaches

This article presents a comprehensive overview of new imaging approaches and techniques for improving the assessment of renal masses and renal cell carcinoma. The Bosniak classification, version 2019, as well as the clear cell likelihood score, version 2.0, will be discussed as new imaging algorithms using established techniques. Additionally, newer modalities, such as contrast-enhanced ultrasound, dual energy computed tomography, and molecular imaging, will be discussed in conjunction with emerging radiomics and artificial intelligence techniques. Current diagnostic algorithms combined with newer approaches may be an effective way to overcome existing limitations in renal mass and RCC characterization.

Radiomic Analysis of Quantitative T2 Mapping and Conventional MRI in Predicting Histologic Grade of Bladder Cancer

We explored the added value of a radiomic strategy based on quantitative transverse relaxation (T2) mapping and conventional magnetic resonance imaging (MRI) to evaluate the histologic grade of bladder cancer (BCa) preoperatively. Patients who were suspected of BCa underwent pelvic MRI (including T2 mapping and diffusion-weighted imaging (DWI) before any treatment. All patients with histological-proved urothelial BCa were included. We constructed different prediction models using the mean signal values and radiomic features from both T2 mapping and apparent diffusion coefficient (ADC) maps. The diagnostic performance of each model or parameter was assessed using receiver operating characteristic curves. In total, 92 patients were finally included (training cohort, n = 64; testing cohort, n = 28); among these, 71 had high-grade BCa. In the testing cohort, the T2-mapping radiomic model achieved the highest prediction performance (area under the curve (AUC), 0.87; 95% confidence interval (CI), 0.73-1.0) compared with the ADC radiomic model (AUC, 0.77; 95%CI, 0.56-0.97), and the joint radiomic model of 0.78 (95%CI, 0.61-0.96). Our results demonstrated that radiomic mapping could provide more information than direct evaluation of T2 and ADC values in differentiating histological grades of BCa. Additionally, among the radiomic models, the T2-mapping radiomic model outperformed the ADC and joint radiomic models.

The Use of Radiomic Tools in Renal Mass Characterization

The incidence of renal mass detection has increased during recent decades, with an increased diagnosis of small renal masses, and a final benign diagnosis in some cases. To avoid unnecessary surgeries, there is an increasing interest in using radiomics tools to predict histological results, using radiological features. We performed a narrative review to evaluate the use of radiomics in renal mass characterization. Conventional images, such as computed tomography (CT) and magnetic resonance (MR), are the most common diagnostic tools in renal mass characterization. Distinguishing between benign and malignant tumors in small renal masses can be challenging using conventional methods. To improve subjective evaluation, the interest in using radiomics to obtain quantitative parameters from medical images has increased. Several studies have assessed this novel tool for renal mass characterization, comparing its ability to distinguish benign to malign tumors, the results in differentiating renal cell carcinoma subtypes, or the correlation with prognostic features, with other methods. In several studies, radiomic tools have shown a good accuracy in characterizing renal mass lesions. However, due to the heterogeneity in the radiomic model building, prospective and external validated studies are needed.

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.

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.

Artificial Intelligence in Renal Cell Carcinoma Histopathology: Current Applications and Future Perspectives

Renal cell carcinoma (RCC) is characterized by its diverse histopathological features, which pose possible challenges to accurate diagnosis and prognosis. A comprehensive literature review was conducted to explore recent advancements in the field of artificial intelligence (AI) in RCC pathology. The aim of this paper is to assess whether these advancements hold promise in improving the precision, efficiency, and objectivity of histopathological analysis for RCC, while also reducing costs and interobserver variability and potentially alleviating the labor and time burden experienced by pathologists. The reviewed AI-powered approaches demonstrate effective identification and classification abilities regarding several histopathological features associated with RCC, facilitating accurate diagnosis, grading, and prognosis prediction and enabling precise and reliable assessments. Nevertheless, implementing AI in renal cell carcinoma generates challenges concerning standardization, generalizability, benchmarking performance, and integration of data into clinical workflows. Developing methodologies that enable pathologists to interpret AI decisions accurately is imperative. Moreover, establishing more robust and standardized validation workflows is crucial to instill confidence in AI-powered systems' outcomes. These efforts are vital for advancing current state-of-the-art practices and enhancing patient care in the future.

Renal Mass Imaging with MRI Clear Cell Likelihood Score: A User's Guide

Small solid renal masses (SRMs) are frequently detected at imaging. Nearly 20% are benign, making careful evaluation with MRI an important consideration before deciding on management. Clear cell renal cell carcinoma (ccRCC) is the most common renal cell carcinoma subtype with potentially aggressive behavior. Thus, confident identification of ccRCC imaging features is a critical task for the radiologist. Imaging features distinguishing ccRCC from other benign and malignant renal masses are based on major features (T2 signal intensity, corticomedullary phase enhancement, and the presence of microscopic fat) and ancillary features (segmental enhancement inversion, arterial-to-delayed enhancement ratio, and diffusion restriction). The clear cell likelihood score (ccLS) system was recently devised to provide a standardized framework for categorizing SRMs, offering a Likert score of the likelihood of ccRCC ranging from 1 (very unlikely) to 5 (very likely). Alternative diagnoses based on imaging appearance are also suggested by the algorithm. Furthermore, the ccLS system aims to stratify which patients may or may not benefit from biopsy. The authors use case examples to guide the reader through the evaluation of major and ancillary MRI features of the ccLS algorithm for assigning a likelihood score to an SRM. The authors also discuss patient selection, imaging parameters, pitfalls, and areas for future development. The goal is for radiologists to be better equipped to guide management and improve shared decision making between the patient and treating physician. © RSNA, 2023 Quiz questions for this article are available in the supplemental material. See the invited commentary by Pedrosa in this issue.

99mTc-MIBI SPECT/CT Evaluation of a Renal Collision Tumor

ABSTRACT

Preoperative differentiation of oncocytomas from renal cell carcinoma (RCC) is often challenging. 99mTc-MIBI imaging could play a potential role in differentiating oncocytoma from RCC, which in turn could guide surgical decision-making. We present the use of 99mTc-MIBI SPECT/CT to characterize a renal mass in a 66-year-old man with a complex medical history, including history of bilateral oncocytomas. 99mTc-MIBI SPECT/CT showed features suspicious of a malignant tumor, which was confirmed postnephrectomy as a chromophobe and papillary RCC collision tumor. This case supports 99mTc-MIBI imaging for preoperative differentiation of benign versus malignant renal tumors.

Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review

Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.

2.5D MFFAU-Net: a convolutional neural network for kidney segmentation

BACKGROUND

Kidney tumors have become increasingly prevalent among adults and are now considered one of the most common types of tumors. Accurate segmentation of kidney tumors can help physicians assess tumor complexity and aggressiveness before surgery. However, segmenting kidney tumors manually can be difficult because of their heterogeneity.

METHODS

This paper proposes a 2.5D MFFAU-Net (multi-level Feature Fusion Attention U-Net) to segment kidneys, tumors and cysts. First, we propose a 2.5D model for learning to combine and represent a given slice in 2D slices, thereby introducing 3D information to balance memory consumption and model complexity. Then, we propose a ResConv architecture in MFFAU-Net and use the high-level and low-level feature in the model. Finally, we use multi-level information to analyze the spatial features between slices to segment kidneys and tumors.

RESULTS

The 2.5D MFFAU-Net was evaluated on KiTS19 and KiTS21 kidney datasets and demonstrated an average dice score of 0.924 and 0.875, respectively, and an average Surface dice (SD) score of 0.794 in KiTS21.

CONCLUSION

The 2.5D MFFAU-Net model can effectively segment kidney tumors, and the results are comparable to those obtained with high-performance 3D CNN models, and have the potential to serve as a point of reference in clinical practice.

Biomarkers for the Detection and Surveillance of Renal Cancer

Renal cell carcinoma (RCC) is a heterogeneous disease characterized by a broad spectrum of disorders in terms of genetics, molecular and clinical characteristics. There is an urgent need for noninvasive tools to stratify and select patients for treatment accurately. In this review, we analyze serum, urinary, and imaging biomarkers that have the potential to detect malignant tumors in patients with RCC. We discuss the characteristics of these numerous biomarkers and their ability to be used routinely in clinical practice. The development of biomarkers continues to evolve with promising prospects.

Multiparametric MRI of Solid Renal Masses: Principles and Applications of Advanced Quantitative and Functional Methods for Tumor Diagnosis and Characterization

Solid renal masses (SRMs) are increasingly detected and encompass both benign and malignant masses, with renal cell carcinoma (RCC) being the most common malignant SRM. Most patients with SRMs will undergo management without a priori pathologic confirmation. There is an unmet need to noninvasively diagnose and characterize RCCs, as significant variability in clinical behavior is observed and a wide range of differing management options exist. Cross-sectional imaging modalities, including magnetic resonance imaging (MRI), are increasingly used for SRM characterization. Multiparametric (mp) MRI techniques can provide insight into tumor biology by probing different physiologic/pathophysiologic processes noninvasively. These include sequences that probe tissue microstructure, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and T₁ relaxometry; oxygen metabolism (blood oxygen level dependent [BOLD-MRI]); as well as vascular flow and perfusion (dynamic contrast-enhanced MRI [DCE-MRI] and arterial spin labeling [ASL]). In this review, we will discuss each mpMRI method in terms of its principles, roles, and discuss the results of human studies for SRM assessment. Future validation of these methods may help to enable a personalized management approach for patients with SRM in the emerging era of precision medicine. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: 2.

"To Be or Not to Be Benign" at Partial Nephrectomy for Presumed RCC Renal Masses: Single-Center Experience with 195 Consecutive Patients

In daily medical practice, an increasing number of kidney masses are being incidentally detected using common imaging techniques, owing to the improved diagnostic accuracy and increasingly frequent use of these techniques. As a consequence, the rate of detection of smaller lesions is increasing considerably. According to certain studies, following surgical treatment, up to 27% of small enhancing renal masses are identified as benign tumors at the final pathological examination. This high rate of benign tumors challenges the appropriateness of surgery for all suspicious lesions, given the morbidity associated with such an intervention. The objective of the present study was, therefore, to determine the incidence of benign tumors at partial nephrectomy (PN) for a solitary renal mass. To meet this end, a total of 195 patients who each underwent one PN for a solitary renal lesion with the intent to cure RCC were included in the final retrospective analysis. A benign neoplasm was identified in 30 of these patients. The age of the patients ranged from 29.9-79 years (average: 60.9 years). The tumor size range was 1.5-7 cm (average: 3 cm). All the operations were successful using the laparoscopic approach. The pathological results were renal oncocytoma in 26 cases, angiomyolipomas in two cases, and cysts in the remaining two cases. In conclusion, we have shown in our present series the incidence rate of benign tumors in patients who have been subjected to laparoscopic PN due to a suspected solitary renal mass. Based on these results, we advise that the patient should be counseled not only about the intra- and post-operative risks of nephron-sparing surgery but also about its dual therapeutic and diagnostic role. Therefore, the patients should be informed of the considerably high probability of a benign histological result.

Molecular Imaging Diagnosis of Renal Cancer Using 99mTc-Sestamibi SPECT/CT and Girentuximab PET-CT-Current Evidence and Future Development of Novel Techniques

Novel molecular imaging opportunities to preoperatively diagnose renal cell carcinoma is under development and will add more value in limiting the postoperative renal function loss and morbidity. We aimed to comprehensively review the research on single photon emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography computed tomography (PET-CT) molecular imaging and to enhance the urologists' and radiologists' knowledge of the current research pattern. We identified an increase in prospective and also retrospective studies that researched to distinguish between benign and malignant lesions and between different clear cell renal cell carcinoma subtypes, with small numbers of patients studied, nonetheless with excellent results on specificity, sensitivity and accuracy, especially for ^99mTc-sestamibi SPECT/CT that delivers quick results compared to a long acquisition time for girentuximab PET-CT, which instead gives better image quality. Nuclear medicine has helped clinicians in evaluating primary and secondary lesions, and has lately returned with new and exciting insights with novel radiotracers to reinforce its diagnostic potential in renal carcinoma. To further limit the renal function loss and post-surgery morbidity, future research is mandatory to validate the results and to clinically implement the diagnostic techniques in the context of precision medicine.

Implications of hydrogen sulfide in colorectal cancer: Mechanistic insights and diagnostic and therapeutic strategies

Hydrogen sulfide (H₂S) is an important signaling molecule in colorectal cancer (CRC). It is produced in the colon by the catalytic synthesis of the colonocytes' enzymatic systems and the release of intestinal microbes, and is oxidatively metabolized in the colonocytes' mitochondria. Both endogenous H₂S in colonic epithelial cells and exogenous H₂S in intestinal lumen contribute to the onset and progression of CRC. The up-regulation of endogenous synthetases is thought to be the cause of the elevated H₂S levels in CRC cells. Different diagnostic probes and combination therapies, as well as tumor treatment approaches through H₂S modulation, have been developed in recent years and have become active area of investigation for the diagnosis and treatment of CRC. In this review, we focus on the specific mechanisms of H₂S production and oxidative metabolism as well as the function of H₂S in the occurrence, progression, diagnosis, and treatment of CRC. We also discuss the present challenges and provide insights into the future research of this burgeoning field.

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

Cross-sectional imaging is the standard diagnostic tool to determine underlying biology in renal masses, which is crucial for subsequent treatment. Currently, standard CT imaging is limited in its ability to differentiate benign from malignant disease. Therefore, various modalities have been investigated to identify imaging-based parameters to improve the noninvasive diagnosis of renal masses and renal cell carcinoma (RCC) subtypes. MRI was reported to predict grading of RCC and to identify RCC subtypes, and has been shown in a small cohort to predict the response to targeted therapy. Dynamic imaging is promising for the staging and diagnosis of RCC. PET/CT radiotracers, such as ¹⁸F-fluorodeoxyglucose (FDG), ¹²⁴I-cG250, radiolabeled prostate-specific membrane antigen (PSMA), and ¹¹C-acetate, have been reported to improve the identification of histology, grading, detection of metastasis, and assessment of response to systemic therapy, and to predict oncological outcomes. Moreover, ⁹⁹Tc-sestamibi and SPECT scans have shown promising results in distinguishing low-grade RCC from benign lesions. Radiomics has been used to further characterize renal masses based on semantic and textural analyses. In preliminary studies, integrated machine learning algorithms using radiomics proved to be more accurate in distinguishing benign from malignant renal masses compared to radiologists' interpretations. Radiomics and radiogenomics are used to complement risk classification models to predict oncological outcomes. Imaging-based biomarkers hold strong potential in RCC, but require standardization and external validation before integration into clinical routines.

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.

Prognostic Significance of 18F-FDG PET/CT Imaging in Survival Outcomes in Patients with Renal Cell Carcinoma

Objectives:

Renal cell carcinoma (RCC) comprises 85%-90% of primary renal malignant tumors originating from the renal tubular epithelium and has different genetic characteristics. This study aimed to investigate the potential predictive role of ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) and metabolic parameters in overall survival (OS) analysis in patients with RCC.

Methods:

¹⁸F-FDG PET/CT images of 100 patients performed for initial staging before surgical or oncological treatments were analyzed retrospectively. Maximum standard uptake value (SUV_max-T) of the primary tumor was calculated and its relationship to patient survival was analyzed. The median follow-up time was 5.61 years (0.01-8.7 years).

Results:

SUV_max-T levels in the patients ranged from 2.1 to 48.9 (median 5.9, mean 9.0±7.9). SUV_max-T was significantly higher in RCC-related death more positive than in the negative cases (p<0.001). However, there was not any statistical significance for gender and pathological subtypes on the survival outcomes of patients (p=0.264 and p=0.784). The patients’ 1-year, 3-year, and 5-year OS rates were 71%, 61%, and 57%, respectively. The highest action of SUV_max-T for estimating OS was a cut-off level of 5.4, which maintained sensitivity and specificity of 81% and 75%, respectively. However, cancer staging remained independent significance for OS (p<0.001).

Conclusion:

SUV_max of primary tumor and cancer stage were demonstrated as significant prognostic factors for OS in patients with RCC. Evaluation of 18F-FDG accumulation with PET/CT may help plan treatment strategies and predict survival outcomes of these patients at diagnosis.

CDH13 is a prognostic biomarker and a potential therapeutic target for patients with clear cell renal cell carcinoma

CDH13 is an atypical member of the cadherin family and is closely related to the clinicopathological factors and prognosis of many types of cancer. However, the role of CDH13 in clear cell renal cell carcinoma (ccRCC) remains unknown. Therefore, we comprehensively analyzed the expression level, diagnostic efficacy, clinical significance, prognostic value, immune infiltration, methylation status, genetic alteration, and biological functions of CDH13 in ccRCC patients. The results showed that CDH13 was significantly upregulated in ccRCC and strongly correlated with better survival, lower cancer stages, and lower tumor grades of ccRCC patients. Additionally, the immune infiltration analysis indicated that CDH13 might play a crucial role in regulating the tumor microenvironment of ccRCC. The results of methylation analysis showed that the epigenetic status of CDH13 was altered, and the prognosis of ccRCC patients was related not only to DNA methylation but also to m6A modification of CDH13. Finally, the results based on clinical samples further elucidated the expression pattern of CDH13 in ccRCC. In conclusion, CDH13 might be a novel prognostic biomarker and therapeutic target for patients with ccRCC. And our study provides new insights into the potential molecular changes and strategies for the treatment of ccRCC.

The multispeciality approach to the management of localised kidney cancer

Historically, kidney cancer was approached in a siloed single-speciality way, with urological surgeons managing the localised stages of the disease and medical oncologists caring for patients if metastases developed. However, improvements in the management of localised kidney cancer have occurred rapidly over the past two decades with greater understanding of the disease biology, diagnostic options, and innovations in curative treatments. These developments are favourable for patients but provide a substantially more complex landscape for patients and clinicians to navigate, with associated challenging decisions about who to treat, how, and when. As such, the skill sets needed to manage the various aspects of the disease and guide patients appropriately outstrips the capabilities of one particular specialist, and the evolution of a multispeciality approach to the management of kidney cancer is now essential. In this Review, we summarise the current best multispeciality practice for the management of localised kidney cancer and the areas in need of further research and development.

PET-CT in Clinical Adult Oncology-IV. Gynecologic and Genitourinary Malignancies

Simple Summary

Positron emission tomography (PET), typically combined with computed tomography (CT), has become a critical advanced imaging technique in oncology. With concurrently acquired positron emission tomography and computed tomography (PET-CT), a radioactive molecule (radiotracer) is injected in the bloodstream and localizes to sites of tumor because of specific cellular features of the tumor that accumulate the targeting radiotracer. The CT scan provides information to allow better visualization of radioactivity from deep or dense structures and to provide detailed anatomic information. PET-CT has a variety of applications in oncology, including staging, therapeutic response assessment, restaging and surveillance. This series of six review articles provides an overview of the value, applications, and imaging interpretive strategies for PET-CT in the more common adult malignancies. The fourth report in this series provides a review of PET-CT imaging in gynecologic and genitourinary malignancies.

Abstract

Concurrently acquired positron emission tomography and computed tomography (PET-CT) is an advanced imaging modality with diverse oncologic applications, including staging, therapeutic assessment, restaging and longitudinal surveillance. This series of six review articles focuses on providing practical information to providers and imaging professionals regarding the best use and interpretative strategies of PET-CT for oncologic indications in adult patients. In this fourth article of the series, the more common gynecological and adult genitourinary malignancies encountered in clinical practice are addressed, with an emphasis on Food and Drug Administration (FDA)-approved and clinically available radiopharmaceuticals. The advent of new FDA-approved radiopharmaceuticals for prostate cancer imaging has revolutionized PET-CT imaging in this important disease, and these are addressed in this report. However, [¹⁸F]F-fluoro-2-deoxy-d-glucose (FDG) remains the mainstay for PET-CT imaging of gynecologic and many other genitourinary malignancies. This information will serve as a guide for the appropriate role of PET-CT in the clinical management of gynecologic and genitourinary cancer patients for health care professionals caring for adult cancer patients. It also addresses the nuances and provides guidance in the accurate interpretation of FDG PET-CT in gynecological and genitourinary malignancies for imaging providers, including radiologists, nuclear medicine physicians and their trainees.

Molecular Heterogeneity Between Paired Primary and Metastatic Lesions from Clear Cell Renal Cell Carcinoma

Highly effective systemic treatments have globally improved outcomes in metastatic clear-cell renal cell carcinoma (m-ccRCC). However, despite many efforts, reliable biomarkers predicting individual responses are currently lacking. Moreover, mixed responses are commonly observed. We hypothesized that molecular heterogeneity between primary tumors and their metastases could flaw biomarker research based on features of the primary tumor and explain mixed responses. Therefore, we studied the heterogeneity of the ccrcc1–4 molecular subtypes across patient-matched primary and metastatic lesions over time in 62 patients with m-ccRCC who underwent both nephrectomy and metastasectomy. These subtypes characterize underlying disease biology and are associated with outcomes in both the primary and metastatic settings. We observed a concordance rate of 58% (95% confidence interval 45–71%). This concordance was not affected by the interval between nephrectomy and resection of the metastatic lesion. Across discordant pairs, the metastatic lesions mostly exhibited a less favorable molecular subtype. Moreover, primary tumors with the favorable ccrcc2 molecular subtype were characterized by favorable prognosis and a long interval between nephrectomy and metastasectomy. Conversely, tumors with the unfavorable ccrcc4 molecular subtype relapsed quickly and had poor prognosis. Thus, the considerable molecular heterogeneity between patient-matched m-ccRCC primary and metastatic lesions provides an explanation for mixed responses to systemic therapy and could impact the development of biomarker studies in which the primary tumor is often considered a surrogate for metastatic disease.

Patient summary

We studied primary tumors and metastases from patients with kidney cancer and found considerable heterogeneity in their molecular features. This heterogeneity explains mixed responses to systemic therapy and is important to take into account in future biomarker studies for this disease.