Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States

Clinical outcome predictors for metastatic renal cell carcinoma: a retrospective multicenter real-life case series

Over the last decades, the therapeutic armamentarium of metastatic renal cell carcinoma (mRCC) has been revolutionized by the advent of tyrosin-kinase inhibitors (TKI), immune-checkpoint inhibitors (ICI), and immune-combinations. RCC is heterogeneous, and even the most used validated prognostic systems, fail to describe its evolution in real-life scenarios. Our aim is to identify potential easily-accessible clinical factors and design a disease course prediction system. Medical records of 453 patients with mRCC receiving sequential systemic therapy in two high-volume oncological centres were reviewed. The Kaplan-Meier method and Cox proportional hazard model were used to estimate and compare survival between groups. As first-line treatment 366 patients received TKI monotherapy and 64 patients received ICI, alone or in combination. The mean number of therapy lines was 2.5. A high Systemic Inflammation Index, a BMI under 25 Kg/m2, the presence of bone metastases before systemic therapy start, age over 65 years at the first diagnosis, non-clear-cell histology and sarcomatoid component were correlated with a worse OS. No significant OS difference was observed between patients receiving combination therapies and those receiving exclusively monotherapies in the treatment sequence. Our relapse prediction system based on pathological stage and histological grade was effective in predicting the time between nephrectomy and systemic treatment. Our multicentric retrospective analysis reveals additional potential prognostic factors for mRCC, not included in current validated prognostic systems, suggests a model for disease course prediction and describes the outcomes of the most common therapeutic strategies currently available.

Genomic Profiling and Molecular Characterization of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) treatment has undergone three major paradigm shifts in recent years, first with the introduction of molecular targeted therapies, then with immune checkpoint inhibitors, and, more recently, with immune-based combinations. However, to date, molecular predictors of response to targeted agents have not been identified for ccRCC. The WHO 2022 classification of renal neoplasms introduced the molecularly defined RCC class, which is a first step in the direction of a better molecular profiling of RCC. We reviewed the literature data on known genomic alterations of clinical interest in ccRCC, discussing their prognostic and predictive role. In particular, we explored the role of VHL, mTOR, chromatin modulators, DNA repair genes, cyclin-dependent kinases, and tumor mutation burden. RCC is a tumor whose pivotal genomic alterations have pleiotropic effects, and the interplay of these effects determines the tumor phenotype and its clinical behavior. Therefore, it is difficult to find a single genomic predictive factor, but it is more likely to identify a signature of gene alterations that could impact prognosis and response to specific treatment. To accomplish this task, the interpolation of large amounts of clinical and genomic data is needed. Nevertheless, genomic profiling has the potential to change real-world clinical practice settings.

Clinico-Pathological Features Influencing the Prognostic Role of Body Mass Index in Patients With Advanced Renal Cell Carcinoma Treated by Immuno-Oncology Combinations (ARON-1)

BACKGROUND

Obesity has been associated with improved response to immunotherapy in cancer patients. We investigated the role of body mass index (BMI) in patients from the ARON-1 study (NCT05287464) treated by dual immuno-oncology agents (IO+IO) or a combination of immuno-oncology drug and a tyrosine kinase inhibitors (TKI) as first-line therapy for metastatic renal cell carcinoma (mRCC).

PATIENTS AND METHODS

Medical records of patients with documented mRCC treated by immuno-oncology combinations were reviewed at 47 institutions from 16 countries. Patients were assessed for overall survival (OS), progression-free survival (OS), and overall clinical benefit (OCB), defined as the sum of the rate of partial/complete responses and stable disease. Univariate and multivariate analyses were used to explore the association of variables of interest with survival.

RESULTS

A total of 675 patients were included; BMI was >25 kg/m2 in 345 patients (51%) and was associated with improved OS (55.7 vs. 28.4 months, P < .001). The OCB of patients with BMI >25 kg/m2 versus those with BMI ≤25 kg/m2 was significantly higher only in patients with nonclear cell histology (81% vs. 65%, P = .011), and patients with liver metastases (76% vs. 58%, P = .007), Neutrophil to lymphocyte ratio >4 (77% vs 62%, P = .022) or treated by nivolumab plus ipilimumab (77% vs. 64%, P = .044). In the BMI ≤25 kg/m2 subgroup, significant differences were found between patients with NLR >4 versus ≤4 (62% vs. 82%, P = .002) and patients treated by IO+IO versus IO+TKIs combinations (64% vs. 83%, P = .002).

CONCLUSION

Our study suggests that the prognostic significance and the association of BMI with treatment outcome varies across clinico-pathological mRCC subgroups.

Artificial Intelligence in Urooncology: What We Have and What We Expect

INTRODUCTION

Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers.

METHODOLOGY

We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors.

RESULTS

Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors.

CONCLUSIONS

AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.

Global Real-World Outcomes of Patients Receiving Immuno-Oncology Combinations for Advanced Renal Cell Carcinoma: The ARON-1 Study

BACKGROUND

Immuno-oncology combinations have achieved survival benefits in patients with metastatic renal cell carcinoma (mRCC).

OBJECTIVE

The ARON-1 study (NCT05287464) was designed to globally collect real-world data on the use of immuno-combinations as first-line therapy for mRCC patients.

PATIENTS AND METHODS

Patients aged ≥ 18 years with a cytologically and/or histologically confirmed diagnosis of mRCC treated with first-line immuno-combination therapies were retrospectively included from 47 International Institutions from 16 countries. Patients were assessed for overall survival (OS), progression-free survival (PFS), and overall clinical benefit (OCB).

RESULTS

A total of 729 patients were included; tumor histology was clear-cell RCC in 86% of cases; 313 patients received dual immuno-oncology (IO + IO) therapy while 416 were treated with IO-tyrosine kinase inhibitor (IO + TKI) combinations. In the overall study population, the median OS and PFS were 36.5 and 15.0 months, respectively. The median OS was longer with IO+TKI compared with IO+IO therapy in the 616 patients with intermediate/poor International mRCC Database Consortium (IMDC) risk criteria (55.7 vs 29.7 months; p = 0.045). OCB was 84% for IO+TKI and 72% for IO + IO combination (p < 0.001).

CONCLUSIONS

Our study may suggest that immuno-oncology combinations are effective as first-line therapy in the mRCC real-world context, showing outcome differences between IO + IO and IO + TKI combinations in mRCC subpopulations.

CLINICAL TRIAL REGISTRATION

NCT05287464.

Role of Clock Genes and Circadian Rhythm in Renal Cell Carcinoma: Recent Evidence and Therapeutic Consequences

Circadian rhythm regulates cellular differentiation and physiology and shapes the immune response. Altered expression of clock genes might lead to the onset of common malignant cancers, including Renal Cell Carcinoma (RCC). Data from Cancer Genome Atlas (TCGA) indicate that clock genes PER1-3, CRY2, CLOCK, NR1D2 and RORα are overexpressed in RCC tissues and correlate with patients' prognosis. The expression of clock genes could finely tune transcription factor activity in RCC and is associated with the extent of immune cell infiltration. The clock system interacts with hypoxia-induced factor-1α (HIF-1α) and regulates the circadian oscillation of mammalian target of rapamycin (mTOR) activity thereby conditioning the antitumor effect of mTOR inhibitors. The stimulation of natural killer (NK) cell activity exerted by the administration of interferon-α, a cornerstone of the first era of immunotherapy for RCC, relevantly varies according to circadian dosing time. Recent evidence demonstrated that time-of-day infusion directly affects the efficacy of immune checkpoint inhibitors in cancer patients. Compounds targeting the circadian clock have been identified and their role in the era of immunotherapy deserves to be further investigated. In this review, we aimed at addressing the impact of clock genes on the natural history of kidney cancer and their potential therapeutic implications.

Segmentation of kidney mass using AgDenseU-Net 2.5D model

The Kidney and Kidney Tumor Segmentation Challenge 2021 (KiTS21) released a kidney CT dataset with 300 patients. Unlike KiTS19, KiTS21 provided a cyst category. Therefore, the segmentation of kidneys, tumors, and cysts will be able to assess the complexity and aggressiveness of kidney mass. Deep learning models can save medical resources, but 3D models still have some disadvantages, such as the high cost of computing resources. This paper proposes a scheme that saves computing resources and achieves the segmentation of kidney mass in two steps. First, we preprocess the kidney volume data using the automatic down-sampling method of 3D images, reducing the volume while preserving the feature information. Second, we finely segment kidneys, tumors, and cysts using the AgDenseU-Net (Attention gate DenseU-Net) 2.5D model. KiTS21 proposed using Hierarchical Evaluation Classes (HECs) to compute a metric for the superset: the HEC of kidney considers kidneys, tumors, and cysts as the foreground to compute segmentation performance; the HEC of kidney mass considers both tumor and cyst as the foreground classes; the HEC of tumor considers tumor as the foreground only. For KiTS21, our model achieved a dice score of 0.971 for the kidney, 0.883 for the mass, and 0.815 for the tumor. In addition, we also tested segmentation results without HECs, and our model achieved a dice score of 0.950 for the kidney, 0.878 for the tumor, and 0.746 for the cyst. The results demonstrate that the method proposed in this paper can be used as a reference for kidney tumor segmentation.

Cabozantinib in Patients with Advanced Renal Cell Carcinoma Primary Refractory to First-line Immunocombinations or Tyrosine Kinase Inhibitors

BACKGROUND

A subset of patients with metastatic renal cell carcinoma (mRCC), deemed as primary refractory, shows progressive disease as the best response to first-line therapy even when treated with novel immune-based combos.

OBJECTIVE

We aimed to assess the outcome of patients treated with second-line cabozantinib for mRCC primary refractory to first-line therapy defined as Response Evaluation Criteria in Solid Tumors (RECIST) progression in the computed tomography scan as the best response to the upfront treatment.

DESIGN, SETTING, AND PARTICIPANTS

We retrospectively collected data from 11 worldwide centers.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan-Meier curves. Cox proportional models were used at univariate and multivariate analyses.

RESULTS AND LIMITATIONS

We collected data from 108 patients with mRCC primary refractory to pembrolizumab plus axitinib (17%), nivolumab plus ipilimumab (36%), or tyrosine kinase inhibitors (TKIs; 31% sunitinib and 16% pazopanib). The median OS with cabozantinib was 9.11 mo, and it was 8.84 and 9.11 mo in patients primary refractory to immunocombinations and TKIs, respectively (p = 0.952). A significant difference was found between patients primary refractory to pembrolizumab plus axitinib (OS not reached) and those primary refractory to nivolumab plus ipilimumab (median OS 8.12 mo, p = 0.024). The median PFS with cabozantinib was 7.30 mo, without significant differences between patients primary refractory to immunocombinations and those primary refractory to TKIs (6.90 vs 7.59 mo, p = 0.435) or between patients primary refractory to pembrolizumab plus axitinib and those primary refractory to nivolumab plus ipilimumab (7.92 and 6.02, p = 0.509). Investigator-assessed overall response rates were 21% and 12% in patients primary refractory to first-line immunocombinations and TKIs, respectively, with a clinical benefit of 48% in the overall population.

CONCLUSIONS

Our data show that cabozantinib is active in primary refractory mRCC patients regardless of which treatment is received as first-line therapy. Systemic options and prognosis of primary refractory patients with mRCC, particularly those treated with novel immune-based combos, are among the major challenges that we need to face in this field.

PATIENT SUMMARY

Patients primary refractory to first-line therapy are characterized by a poor prognosis. Herein, we aimed to assess the outcome of patients treated with second-line cabozantinib for metastatic renal cell carcinoma (mRCC) primary refractory to first-line therapy. Our results suggest that cabozantinib is active in primary refractory mRCC patients.

Concomitant Use of Statins, Metformin, or Proton Pump Inhibitors in Patients with Advanced Renal Cell Carcinoma Treated with First-Line Combination Therapies

BACKGROUND

Drug-drug interactions are a major concern in oncology and may potentially affect the outcome of patients with cancer.

OBJECTIVE

In this study, we aimed to determine whether the concomitant use of statins, metformin, or proton pump inhibitors affects survival in patients with metastatic renal cell carcinoma treated with first-line combination therapies.

METHODS

Medical records of patients with documented metastatic renal cell carcinoma between January 2016 and November 2021 were reviewed at 17 participating centers. This research was conducted in ten institutions, including both referral centers and local hospitals. Patients were assessed for overall survival, progression-free survival, and overall clinical benefit. Univariate and multivariate analyses were conducted to explore the association of variables of interest with overall survival and progression-free survival.

RESULTS

A total of 304 patients receiving dual immunotherapy (51%) or immunotherapy/vascular endothelial growth factor-tyrosine kinase inhibitor (49%) combinations were eligible for inclusion in this retrospective study. Statin use was a significant prognostic factor for longer overall survival in a univariate analysis (hazard ratio 0.48, 95% confidence interval 0.26-0.87; p = 0.016) and a multivariate analysis (hazard ratio 0.48, 95% confidence interval 0.31-0.74; p < 0.001) and was significantly associated with an overall clinical benefit (83% in statin users vs 71% in non-users; p = 0.045). Otherwise, the use of metformin or proton pump inhibitors did not affect the outcome of these patients.

CONCLUSIONS

Our study suggests a prognostic impact of statin use in patients receiving first-line immuno-oncology combinations. The mechanism of this interaction warrants further elucidation.

The Urogenital System’s Role in Diseases: A Synopsis

Simple Summary

The urinary tract microbiome has come under a lot of scrutiny, and this has led to the rejection of the pre-established concept of sterility in the urinary bladder. Microbial communities in the urinary tract have been implicated in the maintenance of health. Thus, alterations in their composition have also been associated with different urinary pathologies, such as urinary tract infections. For that reason, tackling the urinary microbiome of healthy individuals, as well as its involvement in disease through the proliferation of opportunistic pathogens, could open a potential field of study, leading to new insights into prevention, diagnosis, and treatment strategies for different diseases.

Abstract

The human microbiota contains ten times more microbial cells than human cells contained by the human body, constituting a larger genetic material than the human genome itself. Emerging studies have shown that these microorganisms represent a critical determinant in human health and disease, and the use of probiotic products as potential therapeutic interventions to modulate homeostasis and treat disease is being explored. The gut is a niche for the largest proportion of the human microbiota with myriad studies suggesting a strong link between the gut microbiota composition and disease development throughout the body. More specifically, there is mounting evidence on the relevance of gut microbiota dysbiosis in the development of urinary tract disease including urinary tract infections (UTIs), chronic kidney disease, and kidney stones. Fewer emerging reports, however, are suggesting that the urinary tract, which has long been considered ‘sterile’, also houses its unique microbiota that might have an important role in urologic health and disease. The implications of this new paradigm could potentially change the therapeutic perspective in urological disease.

Artificial intelligence-enabled decision support in nephrology

Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and treatment. Emerging evidence suggests that artificial intelligence (AI)-enabled decision support systems - which use algorithms based on learned examples - may have an important role in nephrology. Contemporary AI applications can accurately predict the onset of acute kidney injury before notable biochemical changes occur; can identify modifiable risk factors for chronic kidney disease onset and progression; can match or exceed human accuracy in recognizing renal tumours on imaging studies; and may augment prognostication and decision-making following renal transplantation. Future AI applications have the potential to make real-time, continuous recommendations for discrete actions and yield the greatest probability of achieving optimal kidney health outcomes. Realizing the clinical integration of AI applications will require cooperative, multidisciplinary commitment to ensure algorithm fairness, overcome barriers to clinical implementation, and build an AI-competent workforce. AI-enabled decision support should preserve the pre-eminence of wisdom and augment rather than replace human decision-making. By anchoring intuition with objective predictions and classifications, this approach should favour clinician intuition when it is honed by experience.

Statin use improves the efficacy of nivolumab in patients with advanced renal cell carcinoma

BACKGROUND

Statins are widely used in an ageing population, including subjects with solid malignancies. However, no conclusive evidence is currently available on their potential influence on patients' outcome. We aimed to assess whether statin exposure affects the survival of patients with metastatic renal cell carcinoma (mRCC) treated with nivolumab.

PATIENTS AND METHODS

Medical records of patients with documented mRCC treated with second- or third-line nivolumab were reviewed at ten institutions from Italy, Spain and the USA. Patients were assessed for overall survival (OS), progression-free survival (PFS), and overall clinical benefit. Univariate and multivariate analyses were used to explore the association of variables of interest with survival.

RESULTS

A total of 219 patients with mRCC receiving nivolumab between January 2016 and September 2021 were eligible for inclusion in this study; 59 (27%) were statin users. The median OS (34.4 versus 18.6 months, p = 0.017) and PFS (11.7 versus 4.6 months, p = 0.013) resulted apparently longer in statin users. Stratified by age, longer median OS and PFS were associated with statin exposure in both patients aged ≥70 y (median OS: 21.4 versus 10.1 months, p = 0.047; median PFS: 16.4 versus 4.6 months, p = 0.022) and <70 y (median OS: 34.4 versus 21.4 months, p = 0.043; median PFS: 10.3 versus 4.6 months, p = 0.042). Overall clinical benefit resulted higher in statin users than non-users (71% versus 54%, p = 0.030).

CONCLUSIONS

Our study suggests a prognostic impact of statin use in patients receiving nivolumab for mRCC.

Statins and renal cell carcinoma: Antitumor activity and influence on cancer risk and survival

Statins are commonly prescribed to reduce plasma cholesterol levels and risk of cardiovascular events and mortality. Statin exposure may have cancer-preventive properties in some solid tumors, including Renal Cell Carcinoma (RCC). Emerging evidences show that statins can inhibit RCC cell growth by inducing cell cycle arrest and apoptosis in a dose- and time-dependent manner. In addition, statins inhibit the phosphorylation of AKT, mammalian target of rapamycin (mTOR), and ERK leading to reduced motility of RCC cells. Interestingly, the potential impact of concomitant statin intake has been recently evaluated in RCC patients treated by targeted therapy or immunotherapy. In this review, we illustrate the most recent data on the preclinical activity of statins in Renal Cell Carcinoma models and discuss the impact of their use on the prevention and survival of patients affected by this tumor.

CD105: tumor diagnosis, prognostic marker and future tumor therapeutic target

Cancer is one of the diseases with the highest morbidity and mortality rates worldwide, and its therapeutic options are inadequate. The endothelial glycoprotein, also known as CD105, is a type I transmembrane glycoprotein located on the surface of the cell membranes and it is one of the transforming growth factor-β (TGF-β) receptor complexes. It regulates the responses associated with binding to transforming growth factor β1 egg (Activin-A), bone morphogenetic protein 2 (BMP-2), and bone morphogenetic protein 7 (BMP-7). Additionally, it is involved in the regulation of angiogenesis. This glycoprotein is indispensable in the treatment of tumor angiogenesis, and it also plays a leading role in tumor angiogenesis therapy. Therefore, CD105 is considered to be a novel therapeutic target. In this study, we explored the significance of CD105 in the diagnosis, treatment and prognosis of various tumors, and provided evidence for the effect and mechanism of CD105 on tumors.

Concomitant Proton Pump Inhibitors and Outcome of Patients Treated with Nivolumab Alone or Plus Ipilimumab for Advanced Renal Cell Carcinoma

BACKGROUND

Immune checkpoint inhibitors (ICIs) represent the standard of care as first- or second-line treatment in patients with renal cell carcinoma (RCC). Proton pump inhibitors (PPIs) are among the most prescribed drugs worldwide and are known to affect gut microbiota, which is gaining interest in its association with outcomes for patients on ICIs.

OBJECTIVE

The aim of this study was to evaluate the impact of PPIs on outcomes in RCC patients receiving immunotherapy.

PATIENTS AND METHODS

We retrospectively collected data from patients with metastatic RCC who received the combination of ipilimumab and nivolumab for first-line treatment (Cohort 1) or single-agent nivolumab for second-line or third-line treatment (Cohort 2) from five international centers with expertise in the treatment of RCC. Data about clinicopathological characteristics, PPI use, and outcome on ICIs were collected. Endpoints of the study were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS).

RESULTS

Two hundred and eighteen patients (71% male, median age 61 years) were included in the analysis, 62 in Cohort 1 (including 25 patients receiving PPIs) and 156 in Cohort 2 (including 88 patients receiving PPIs), and were followed up for a median of 42 months. In Cohort 1, no difference was observed in ORR (48% vs 57%; p = 0.203), PFS (12.2 vs 8.5 months; p = 0.928), or OS (not reached [NR] vs 27.3 months; p = 0.84). In Cohort 2, no difference was observed in ORR (32% vs 28%; p = 0.538), PFS (6.7 vs 9.0 months; p = 0.799), or OS (16.0 vs 26.0 months; p = 0.324).

CONCLUSIONS

In patients with RCC, concomitant PPI use did not seem to affect survival outcomes on ICIs, either as combination therapy or monotherapy.

The role of stereotactic body radiation therapy and its integration with systemic therapies in metastatic kidney cancer: a multicenter study on behalf of the AIRO (Italian Association of Radiotherapy and Clinical Oncology) genitourinary study group

Although systemic therapy represents the standard of care for polymetastatic kidney cancer, stereotactic body radiation therapy (SBRT) may play a relevant role in the oligometastatic setting. We conducted a multicenter study including oligometastatic kidney cancer treated with SBRT. We retrospectively analyzed 207 patients who underwent 245 SBRT treatments on 385 lesions, including 165 (42.9%) oligorecurrent (OR) and 220 (57.1%) oligoprogressive (OP) lesions. Most common sites were lung (30.9%) for OR group, and bone (32.7%) for OP group. Among 78 (31.8%) patients receiving concomitant systemic therapy, sunitinib (61.5%) and pazopanib (15.4%) were the most common for OR patients, while sunitinib (49.2%) and nivolumab (20.0%) for OP patients. End points were local control (LC), progression free survival (PFS), overall survival (OS), time to next systemic therapy (TTNS) and toxicity. Median follow-up was 18.6 months. 1, 2 and 3-year LC rates were 89.4%, 80.1% and 76.6% in OR patients, and 82.7%, 76.9% and 64.3% in those with OP, respectively. LC for OP group was influenced by clear cell histology (p = 0.000), total number of lesions (p = 0.004), systemic therapy during SBRT (p = 0.012), and SBRT dose (p = 0.012). Median PFS was 37.9 months. 1, 2- and 3-year OS was 92.7%, 86.4% and 81.8%, respectively. Median TTNS was 15.8 months for OR patients, and 13.9 months for OP patients. No grade 3 or higher toxicities were reported for both groups. SBRT may be considered an effective safe option in the multidisciplinary management of both OR and OP metastases from kidney cancer.

Intelligent Computing with Levenberg-Marquardt Backpropagation Neural Networks for Third-Grade Nanofluid Over a Stretched Sheet with Convective Conditions

This article discussed the influence of activation energy on MHD flow of third-grade nanofluid model (MHD-TGNFM) along with the convective conditions and used the technique of backpropagation in artificial neural network using Levenberg–Marquardt technique (BANN-LMT). The PDEs representing (MHD-TGNFM) transformed into the system of ODEs. The dataset for BANN-LMT is computed for the six scenarios by using the Adam numerical method by varying the local Hartman number (Ha), Prandtl number (Pr), local chemical reaction parameter (σ), Schmidt number (Sc), concentration Biot number (γ₂) and thermal Biot number (γ₁). By testing, validation and training process of (BANN-LMT), the estimated solutions are interpreted for (MHD-TGNFM). The validation of the performance of (BANN-LMT) is done through the MSE, error histogram and regression analysis. The concentration profile increases when there is an increase in Biot number and the local Hartmann number; meanwhile, it decreases for the higher values of Schmidt number and the local chemical reaction parameter.

Cabozantinib in Pretreated Patients with Metastatic Renal Cell Carcinoma with Sarcomatoid Differentiation: A Real-World Study

BACKGROUND

Renal cell carcinoma with sarcomatoid differentiation is a highly aggressive form of kidney cancer.

OBJECTIVE

We aimed to analyze the outcomes of patients treated with cabozantinib for metastatic renal cell carcinoma with sarcomatoid features.

METHODS

We retrospectively collected data from 16 worldwide centers. Overall survival and progression-free survival were analyzed using Kaplan-Meier curves. Cox proportional models were used for univariate and multivariate analyses.

RESULTS

We collected data from 66 patients with metastatic sarcomatoid renal cell carcinoma receiving cabozantinib as second-line (51%) or third-line (49%) therapy. The median progression-free survival from the start of cabozantinib was 7.59 months (95% confidence interval [CI] 5.75-17.49) and was longer in male patients (8.81 vs 5.95 months, p = 0.042) and in patients without bone metastases (7.59 vs 5.11 months, p = 0.010); the median overall survival was 9.11 months (95% CI 7.13-23.80). At the multivariate analysis, female sex (hazard ratio = 1.81; 95% CI 1.02-3.37, p = 0.046), bone metastases (hazard ratio = 2.62; 95% CI 1.34-5.10, p = 0.005), and International Metastatic Renal Cell Carcinoma Database Consortium criteria (hazard ratio = 3.04; 95% CI 1.54-5.99, p = 0.001) were significant predictors of worse overall survival.

CONCLUSIONS

Our data show that cabozantinib is active in pretreated patients with sarcomatoid renal cell carcinoma. Biomarkers are needed in this field to select patients for multi-kinase inhibitors or other options.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Body Mass Index in Patients Treated with Cabozantinib for Advanced Renal Cell Carcinoma: A New Prognostic Factor?

We analyzed the clinical and pathological features of renal cell carcinoma (RCC) patients treated with cabozantinib stratified by body mass index (BMI). We retrospectively collected data from 16 worldwide centers involved in the treatment of RCC. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan–Meier curves. Cox proportional models were used at univariate and multivariate analyses. We collected data from 224 patients with advanced RCC receiving cabozantinib as second- (113, 5%) or third-line (111, 5%) therapy. The median PFS was significantly higher in patients with BMI ≥ 25 (9.9 vs. 7.6 months, p < 0.001). The median OS was higher in the BMI ≥ 25 subgroup (30.7 vs. 11.0 months, p = 0.003). As third-line therapy, both median PFS (9.2 months vs. 3.9 months, p = 0.029) and OS (39.4 months vs. 11.5 months, p = 0.039) were longer in patients with BMI ≥ 25. BMI was a significant predictor for both PFS and OS at multivariate analysis. We showed that a BMI ≥ 25 correlates with longer survival in patients receiving cabozantinib. BMI can be easily assessed and should be included in current prognostic criteria for advanced RCC.