Adrenal fast-track and enhanced recovery in retroperitoneoscopic surgery for primary aldosteronism improving patient outcome and efficiency.

PURPOSE: No data exist on perioperative strategies for enhancing recovery after posterior retroperitoneoscopic adrenalectomy (PRA). Our objective was to determine whether a multimodality adrenal fast-track and enhanced recovery (AFTER) protocol for PRA can reduce recovery time, improve patient satisfaction and maintain safety. METHODS: Thirty primary aldosteronism patients were included. Fifteen patients were treated with 'standard-of-care' PRA and compared with 15 in the AFTER protocol. The AFTER protocol contains: a preoperative information video, postoperative oral analgesics, early postoperative mobilisation and enteral feeding, and blood pressure monitoring at home. The primary outcome was recovery time. Secondary outcomes were length of hospital stay, postoperative pain and analgesics requirements, patient satisfaction, perioperative complications and quality of life (QoL). RESULTS: Recovery time was much shorter in both groups than anticipated and was not significantly different (median 28 days). Postoperative length of hospital stay was significantly reduced in AFTER patients (mean 32 vs 42 h, CI 95%, p = 0.004). No significant differences were seen in pain, but less analgesics were used in the AFTER group. Satisfaction improved amongst AFTER patients for time of admission and postoperative visit to the outpatient clinic. There were no significant differences in complication rates or QoL. CONCLUSION: Despite no difference in recovery time between the two groups, probably due to small sample size, the AFTER protocol led to shorter hospital stays and less analgesic use after surgery, whilst maintaining and even enhancing patient satisfaction for several aspects of perioperative care. Complication rates and QoL are comparable to standard-of-care.

Autistic and non-autistic individuals show the same amygdala activity during emotional face processing.

BACKGROUND: Autistic and non-autistic individuals often differ in how they perceive and show emotions, especially in their ability and inclination to infer other people's feelings from subtle cues like facial expressions. Prominent theories of autism have suggested that these differences stem from alterations in amygdala functioning and that amygdala hypoactivation causes problems with emotion recognition. Thus far, however, empirical investigations of this hypothesis have yielded mixed results and largely relied on relatively small samples. METHODS: In a sample of 72 autistic and 79 non-autistic participants, we conducted a study in which we used the Hariri paradigm to test whether amygdala activation during emotional face processing is altered in autism spectrum disorder, and whether common mental disorders like depression, ADHD or anxiety disorders influence any potential alterations in activation patterns. RESULTS: We found no evidence for differences in amygdala activation, neither when comparing autistic and non-autistic participants, nor when taking into account mental disorders or the overall level of functional impairment. LIMITATIONS: Because we used one basic emotion processing task in a Dutch sample, results might not generalise to other tasks and other populations. CONCLUSIONS: Our results challenge the view that autistic and non-autistic processing of emotional faces in the amygdala is vastly different and call for a more nuanced view of differences between non-autistic and autistic emotion processing.

Multi-polygenic scores in psychiatry: From disorder specific to transdiagnostic perspectives.

The dense co-occurrence of psychiatric disorders questions the categorical classification tradition and motivates efforts to establish dimensional constructs with neurobiological foundations that transcend diagnostic boundaries. In this study, we examined the genetic liability for eight major psychiatric disorder phenotypes under both a disorder-specific and a transdiagnostic framework. The study sample (n = 513) was deeply phenotyped, consisting of 452 patients from tertiary care with mood disorders, anxiety disorders (ANX), attention-deficit/hyperactivity disorder (ADHD), autism spectrum disorders, and/or substance use disorders (SUD) and 61 unaffected comparison individuals. We computed subject-specific polygenic risk score (PRS) profiles and assessed their associations with psychiatric diagnoses, comorbidity status, as well as cross-disorder behavioral dimensions derived from a rich battery of psychopathology assessments. High PRSs for depression were unselectively associated with the diagnosis of SUD, ADHD, ANX, and mood disorders (p < 1e-4). In the dimensional approach, four distinct functional domains were uncovered, namely the negative valence, social, cognitive, and regulatory systems, closely matching the major functional domains proposed by the Research Domain Criteria (RDoC) framework. Critically, the genetic predisposition for depression was selectively reflected in the functional aspect of negative valence systems (R2 = 0.041, p = 5e-4) but not others. This study adds evidence to the ongoing discussion about the misalignment between current psychiatric nosology and the underlying psychiatric genetic etiology and underscores the effectiveness of the dimensional approach in both the functional characterization of psychiatric patients and the delineation of the genetic liability for psychiatric disorders.

Development and validation of a multimodal neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis.

BACKGROUND: Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, monocenter studies indicate that both structural magnetic resonance imaging (sMRI) and functional MRI (fMRI) biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. The objective of this study was to develop and validate neuroimaging biomarkers for ECT outcome in a multicenter setting. METHODS: Multimodal data (i.e. clinical, sMRI and resting-state fMRI) were collected from seven centers of the Global ECT-MRI Research Collaboration (GEMRIC). We used data from 189 depressed patients to evaluate which data modalities or combinations thereof could provide the best predictions for treatment remission (HAM-D score ⩽7) using a support vector machine classifier. RESULTS: Remission classification using a combination of gray matter volume and functional connectivity led to good performing models with average 0.82-0.83 area under the curve (AUC) when trained and tested on samples coming from the three largest centers (N = 109), and remained acceptable when validated using leave-one-site-out cross-validation (0.70-0.73 AUC). CONCLUSIONS: These results show that multimodal neuroimaging data can be used to predict remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. Future development of a clinical decision support tool applying these biomarkers may be feasible.

Mechanism of Action of ECT in Depression.

Electroconvulsive therapy (ECT) remains the most potent antidepressant treatment available for patients with major depressive disorder (MDD). ECT is highly effective, achieving a response rate of 70-80% and a remission rate of 50-60% even in treatment-resistant patients. The underlying mechanisms of ECT are not fully understood, although several hypotheses have been proposed, including the monoamine hypothesis, anticonvulsive hypothesis, neuroplastic effects, and immunomodulatory properties. In this paper, we provide an overview of magnetic resonance imaging evidence that addresses the neuroplastic changes that occur after ECT at the human systems level and elaborate further on ECTs potent immunomodulatory properties. Despite a growing body of evidence that suggests ECT may normalize many of the structural and functional changes in the brain associated with severe depression, there is a lack of convergence between neurobiological changes and the robust clinical effects observed in depression. This may be due to sample sizes used in ECT studies being generally small and differences in data processing and analysis pipelines. Collaborations that acquire large datasets, such as the GEMRIC consortium, can help translate ECT's clinical efficacy into a better understanding of its mechanisms of action.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this causal depression network (CDN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis Principal Component Analysis (PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CDN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CDN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes (t = -2.35, p = 0.019). This evidence further supports that treatment interventions converge on a CDN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

TripleAiM1: a nationwide registry of de novo metastatic hormone-sensitive prostate cancer with prospective quality-of-life assessment.

INTRODUCTION: The treatment landscape for de novo metastatic hormone sensitive prostate cancer (mHSPC) is rapidly evolving. With an abundance of available treatment strategies, selecting the optimal strategy for an individual patient is becoming increasingly challenging. TripleAiM1 aims to evaluate the impact of mHSPC treatments on health-related quality of life (HRQoL) and to provide real-world data insights on diagnostics, treatment strategies, patient subgroups and related healthcare expenditure for mHSPC. The aspirational target of TripleAiM1 is that in the near future, a more tailored therapy can be offered based on the individual patient's wishes and needs in accordance with the overarching principle of value-based healthcare. METHODS AND ANALYSIS: We describe the TripleAiM1 study design; a nationwide registry comprising a retrospective and prospective cohort of patients with de novo mHSPC. Starting in May 2020, eligible patients are identified, selected and recruited in 14 participating hospitals in the Netherlands. Our hypothesis is that, in a real-world setting, differences in clinically meaningful HRQoL deterioration will be observed for treatment strategies over time. HRQoL data, assessed with patient-reported outcome measures, costs and clinical data will be collected for 24 months.For the retrospective cohort, all patients diagnosed with de novo mHSPC from January 2017 onwards are eligible for inclusion. Patient and tumour characteristics, imaging modalities and treatment patterns will be analysed descriptively to provide a real-world overview.Time-to-event endpoints will be assessed using the Kaplan-Meier method and regression models will be employed to analyse baseline characteristics associated with an increased likelihood of death, progression and HRQoL deterioration. Longitudinal mixed-effects models will be employed to assess change of patient-reported outcome scores from baseline until the end of follow-up. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Medical Research Ethics Committee, Twente. Study results will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NL9719.

Reliability and Efficiency of the CAPRI-3 Metastatic Prostate Cancer Registry Driven by Artificial Intelligence.

BACKGROUND: Manual data collection is still the gold standard for disease-specific patient registries. However, CAPRI-3 uses text mining (an artificial intelligence (AI) technology) for patient identification and data collection. The aim of this study is to demonstrate the reliability and efficiency of this AI-driven approach. METHODS: CAPRI-3 is an observational retrospective multicenter cohort registry on metastatic prostate cancer. We tested the patient-identification algorithm and automated data extraction through manual validation of the same patients in two pilots in 2019 and 2022. RESULTS: Pilot one identified 2030 patients and pilot two 9464 patients. The negative predictive value of the algorithm was maximized to prevent false exclusions and reached 94.8%. The completeness and accuracy of the automated data extraction were 92.3% or higher, except for date fields and inaccessible data (images/pdf) (10-88.9%). Additional manual quality control took over 3 h less time per patient than the original fully manual CAPRI registry (105 vs. 300 min). CONCLUSIONS: The CAPRI-3 patient-identification algorithm is a sound replacement for excluding ineligible candidates. The AI-driven data extraction is largely accurate and complete, but manual quality control is needed for less reliable and inaccessible data. Overall, the AI-driven approach of the CAPRI-3 registry is reliable and timesaving.

Electroconvulsive therapy-induced volumetric brain changes converge on a common causal circuit in depression.

Neurostimulation is a mainstream treatment option for major depression. Neuromodulation techniques apply repetitive magnetic or electrical stimulation to some neural target but significantly differ in their invasiveness, spatial selectivity, mechanism of action, and efficacy. Despite these differences, recent analyses of transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS)-treated individuals converged on a common neural network that might have a causal role in treatment response. We set out to investigate if the neuronal underpinnings of electroconvulsive therapy (ECT) are similarly associated with this common causal network (CCN). Our aim here is to provide a comprehensive analysis in three cohorts of patients segregated by electrode placement (N = 246 with right unilateral, 79 with bitemporal, and 61 with mixed) who underwent ECT. We conducted a data-driven, unsupervised multivariate neuroimaging analysis (Principal Component Analysis, PCA) of the cortical and subcortical volume changes and electric field (EF) distribution to explore changes within the CCN associated with antidepressant outcomes. Despite the different treatment modalities (ECT vs TMS and DBS) and methodological approaches (structural vs functional networks), we found a highly similar pattern of change within the CCN in the three cohorts of patients (spatial similarity across 85 regions: r = 0.65, 0.58, 0.40, df = 83). Most importantly, the expression of this pattern correlated with clinical outcomes. This evidence further supports that treatment interventions converge on a CCN in depression. Optimizing modulation of this network could serve to improve the outcome of neurostimulation in depression.

Effective resting-state connectivity in severe unipolar depression before and after electroconvulsive therapy.

BACKGROUND: Electroconvulsive therapy (ECT) is one of the most effective treatments for severe depressive disorders. A recent multi-center study found no consistent changes in correlation-based (undirected) resting-state connectivity after ECT. Effective (directed) connectivity may provide more insight into the working mechanism of ECT. OBJECTIVE: We investigated whether there are consistent changes in effective resting-state connectivity. METHODS: This multi-center study included data from 189 patients suffering from severe unipolar depression and 59 healthy control participants. Longitudinal data were available for 81 patients and 24 healthy controls. We used dynamic causal modeling for resting-state functional magnetic resonance imaging to determine effective connectivity in the default mode, salience and central executive networks before and after a course of ECT. Bayesian general linear models were used to examine differences in baseline and longitudinal effective connectivity effects associated with ECT and its effectiveness. RESULTS: Compared to controls, depressed patients showed many differences in effective connectivity at baseline, which varied according to the presence of psychotic features and later treatment outcome. Additionally, effective connectivity changed after ECT, which was related to ECT effectiveness. Notably, treatment effectiveness was associated with decreasing and increasing effective connectivity from the posterior default mode network to the left and right insula, respectively. No effects were found using correlation-based (undirected) connectivity. CONCLUSIONS: A beneficial response to ECT may depend on how brain regions influence each other in networks important for emotion and cognition. These findings further elucidate the working mechanisms of ECT and may provide directions for future non-invasive brain stimulation research.

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.

Reviewing the neurobiology of electroconvulsive therapy on a micro- meso- and macro-level.

BACKGROUND: Electroconvulsive therapy (ECT) remains the one of the most effective of biological antidepressant interventions. However, the exact neurobiological mechanisms underlying the efficacy of ECT remain unclear. A gap in the literature is the lack of multimodal research that attempts to integrate findings at different biological levels of analysis METHODS: We searched the PubMed database for relevant studies. We review biological studies of ECT in depression on a micro- (molecular), meso- (structural) and macro- (network) level. RESULTS: ECT impacts both peripheral and central inflammatory processes, triggers neuroplastic mechanisms and modulates large scale neural network connectivity. CONCLUSIONS: Integrating this vast body of existing evidence, we are tempted to speculate that ECT may have neuroplastic effects resulting in the modulation of connectivity between and among specific large-scale networks that are altered in depression. These effects could be mediated by the immunomodulatory properties of the treatment. A better understanding of the complex interactions between the micro-, meso- and macro- level might further specify the mechanisms of action of ECT.

Unpacking the functional heterogeneity of the Emotional Face Matching Task: a normative modelling approach.

Functional neuroimaging has contributed substantially to understanding brain function but is dominated by group analyses that index only a fraction of the variation in these data. It is increasingly clear that parsing the underlying heterogeneity is crucial to understand individual differences and the impact of different task manipulations. We estimate large-scale (N=7728) normative models of task-evoked activation during the Emotional Face Matching Task, which enables us to bind heterogeneous datasets to a common reference and dissect heterogeneity underlying group-level analyses. We apply this model to a heterogenous patient cohort, to map individual differences between patients with one or more mental health diagnoses relative to the reference cohort and determine multivariate associations with transdiagnostic symptom domains. For the face>shapes contrast, patients have a higher frequency of extreme deviations which are spatially heterogeneous. In contrast, normative models for faces>baseline have greater predictive value for individuals' transdiagnostic functioning.

[89Zr]Zr-DFO-girentuximab and [18F]FDG PET/CT to Predict Watchful Waiting Duration in Patients with Metastatic Clear-cell Renal Cell Carcinoma.

PURPOSE: Watchful waiting (WW) can be considered for patients with metastatic clear-cell renal cell carcinoma (mccRCC) with good or intermediate prognosis, especially those with <2 International Metastatic RCC Database Consortium criteria and ≤2 metastatic sites [referred to as watch and wait ("W&W") criteria]. The IMaging PAtients for Cancer drug SelecTion-Renal Cell Carcinoma study objective was to assess the predictive value of [18F]FDG PET/CT and [89Zr]Zr-DFO-girentuximab PET/CT for WW duration in patients with mccRCC. EXPERIMENTAL DESIGN: Between February 2015 and March 2018, 48 patients were enrolled, including 40 evaluable patients with good (n = 14) and intermediate (n = 26) prognosis. Baseline contrast-enhanced CT, [18F]FDG and [89Zr]Zr-DFO-girentuximab PET/CT were performed. Primary endpoint was the time to disease progression warranting systemic treatment. Maximum standardized uptake values (SUVmax) were measured using lesions on CT images coregistered to PET/CT. High and low uptake groups were defined on the basis of median geometric mean SUVmax of RECIST-measurable lesions across patients. RESULTS: The median WW time was 16.1 months [95% confidence interval (CI): 9.0-31.7]. The median WW period was shorter in patients with high [18F]FDG tumor uptake than those with low uptake (9.0 vs. 36.2 months; HR, 5.6; 95% CI: 2.4-14.7; P < 0.001). Patients with high [89Zr]Zr-DFO-girentuximab tumor uptake had a median WW period of 9.3 versus 21.3 months with low uptake (HR, 1.7; 95% CI: 0.9-3.3; P = 0.13). Patients with "W&W criteria" had a longer median WW period of 21.3 compared with patients without: 9.3 months (HR, 1.9; 95% CI: 0.9-3.9; Pone-sided = 0.034). Adding [18F]FDG uptake to the "W&W criteria" improved the prediction of WW duration (P < 0.001); whereas [89Zr]Zr-DFO-girentuximab did not (P = 0.53). CONCLUSIONS: In patients with good- or intermediate-risk mccRCC, low [18F]FDG uptake is associated with prolonged WW. This study shows the predictive value of the "W&W criteria" for WW duration and shows the potential of [18F]FDG-PET/CT to further improve this.

Structural Changes in Depressed Patients Directly After Treatment With Electroconvulsive Therapy and 3 Months Later.

OBJECTIVES: It is assumed that neuroplasticity plays a central role in the effect of electroconvulsive therapy (ECT) on patients with major depressive disorder. We carried out an explorative study to map out the extent in which gray matter volume changes could be found directly after ECT treatment and after follow-up. METHODS: Initially, 12 patients with treatment-resistant depression were recruited from the Radboud Medical Center. Magnetic resonance imaging scans were conducted at the following 3 time points: before ECT (n = 12), after ECT (n = 10), and at 3-month follow-up (n = 8). Subcortical volume, hippocampal subfield volume, and cortical thickness were analyzed using FreeSurfer. RESULTS: The extensive, generalized changes in gray matter volume are largely transient after treatment with ECT, with the noted exceptions being a sustained increase in volume of the right amygdala and a part of the left cornu ammonis. Post hoc testing revealed no significant correlation with clinical response. DISCUSSION: Our results suggest that the neuroplastic effects of ECT may not be mediators of clinical response and could be transient epiphenomena.

Striatal connectopic maps link to functional domains across psychiatric disorders.

Transdiagnostic approaches to psychiatry have significant potential in overcoming the limitations of conventional diagnostic paradigms. However, while frameworks such as the Research Domain Criteria have garnered significant enthusiasm among researchers and clinicians from a theoretical angle, examples of how such an approach might translate in practice to understand the biological mechanisms underlying complex patterns of behaviors in realistic and heterogeneous populations have been sparse. In a richly phenotyped clinical sample (n = 186) specifically designed to capture the complex nature of heterogeneity and comorbidity within- and between stress- and neurodevelopmental disorders, we use exploratory factor analysis on a wide range of clinical questionnaires to identify four stable functional domains that transcend diagnosis and relate to negative valence, cognition, social functioning and inhibition/arousal before replicating them in an independent dataset (n = 188). We then use connectopic mapping to map inter-individual variation in fine-grained topographical organization of functional connectivity in the striatum-a central hub in motor, cognitive, affective and reward-related brain circuits-and use multivariate machine learning (canonical correlation analysis) to show that these individualized topographic representations predict transdiagnostic functional domains out of sample (r = 0.20, p = 0.026). We propose that investigating psychiatric symptoms across disorders is a promising path to linking them to underlying biology, and can help bridge the gap between neuroscience and clinical psychiatry.

White matter changes following electroconvulsive therapy for depression: a multicenter ComBat harmonization approach.

ECT is proposed to exert a therapeutic effect on WM microstructure, but the limited power of previous studies made it difficult to highlight consistent patterns of change in diffusion metrics. We initiated a multicenter analysis and sought to address whether changes in WM microstructure occur following ECT. Diffusion tensor imaging (DTI) data (n = 58) from 4 different sites were harmonized before pooling them by using ComBat, a batch-effect correction tool that removes inter-site technical variability, preserves inter-site biological variability, and maximizes statistical power. Downstream statistical analyses aimed to quantify changes in Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD), by employing whole-brain, tract-based spatial statistics (TBSS). ECT increased FA in the right splenium of the corpus callosum and the left cortico-spinal tract. AD in the left superior longitudinal fasciculus and the right inferior fronto-occipital fasciculus was raised. Increases in MD and RD could be observed in overlapping white matter structures of both hemispheres. At baseline, responders showed significantly smaller FA values in the left forceps major and smaller AD values in the right uncinate fasciculus compared with non-responders. By harmonizing multicenter data, we demonstrate that ECT modulates altered WM microstructure in important brain circuits that are implicated in the pathophysiology of depression. Furthermore, responders appear to present a more decreased WM integrity at baseline which could point toward a specific subtype of patients, characterized by a more altered neuroplasticity, who are especially sensitive to the potent neuroplastic effects of ECT.

Clinical use of the mRNA urinary biomarker SelectMDx test for prostate cancer.

BACKGROUND: Molecular biomarker tests are developed as diagnostic tools for prostate cancer (PCa) diagnosis. The SelectMDx (MDxHealth, Nijmegen, The Netherlands) test is a urinary-based biomarker test intended to be used to predict presence of high-grade PCa upon biopsy in men with elevated serum prostate-specific antigen (PSA) levels. Previous validation of the SelectMDx test revealed that 53% of the unnecessary biopsies (biopsies indicating no- or GG1 PCa) could be avoided using the SelectMDx test as a decision-tool to select men for prostate biopsy. The objective of this study is to examine the use of the commercially available SelectMDx test under routine, real-life practice. METHODS: Men that underwent a SelectMDx test between May 2019 and December 2020 and that were originating from countries that perform the SelectMDx test on a regular basis were included in this study, resulting in 5157 cases from 10 European countries. Clinical parameters, urinary RNA scores, and test outcomes were compared between PSA groups, age groups, countries, and the validation cohort (described previously [4]) using the Mann-Whitney U test, Chi-Square test, Benjamini-Hochberg and Kruskal-Wallis tests. RESULTS: 40.72% of the cases received a negative SelectMDx result. The test is also used in patients outside the intended-use population (PSA < 3 and >10 ng/mL). Clinical parameters (age, PSA density, DRE outcome) varied between patient population from individual countries and the validation cohort, resulting in differences in the potential number of saved biopsies using the test. CONCLUSIONS: The potential number of reduced biopsies in clinical use was 40,72% using the SelectMDx test, assuming a negative SelectMDx test resulted in the decision not to biopsy the patient. This is higher compared to the validation cohort, which is explained by differences in patient population.

Combination of sunitinib and 177Lu-labeled antibody cG250 targeted radioimmunotherapy: A promising new therapeutic strategy for patients with advanced renal cell cancer.

Sunitinib is an effective treatment for patients with metastatic Renal Cell Carcinoma (mRCC) but ultimately resistance occurs. The aim of this study was to investigate sunitinib resistance in RCCs and to develop therapeutic combination strategies with targeted radioimmunotherapy (RIT). We studied two RCC models, analyzed Vascular endothelial growth factor (VEGF) and its receptor (VEGFR) and AXL/MET expression and performed therapy studies in Balb/cnu/nu mice combining sunitinib and [177Lu]Lu-cG250 RIT (6.5 MBq/10 µg), specifically targeting RCC cells. pAXL and pMET were expressed in sunitinib-resistant SK-RC-52 and absent in sunitinib-sensitive NU12. NGS evaluation showed that expression of VEGFA, VEGFB, VEGFD, PGF and VEGFR1,2,3 was higher and expression of VEGFC and PDGFA was lower in NU12 than in SK-RC-52. Therapy studies combining sunitinib with [177Lu]Lu-cG250 RIT showed that the best response in mice with "resistant" SK-RC-52 tumors was observed with two cycles of Sunitinib and [177Lu]Lu-cG250 RIT, probably due to increased vascular permeability by sunitinib treatment. In the "sensitive" NU12 model, two cycles of [177Lu]Lu-cG250 RIT and two cycles of combination treatment were equally effective. Enhanced therapeutic efficacy was achieved when two agents ([177Lu]Lu-cG250 RIT and sunitinib) that on their own did not induce satisfactory response levels, are combined. Our findings provide a promising new therapeutic strategy for patients with advanced RCC.