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Objectives: To evaluate psychological, social, and financial outcomes amongst individuals undergoing a non-contrast abdominal computed tomography (CT) scan to screen for kidney cancer and other abdominal malignancies alongside the thoracic CT within lung cancer screening., Subjects and Methods: The Yorkshire Kidney Screening Trial (YKST) is a feasibility study of adding a non-contrast abdominal CT scan to the thoracic CT within lung cancer screening. A total of 500 participants within the YKST, comprising all who had an abnormal CT scan and a random sample of one-third of those with a normal scan between 14/03/2022 and 24/08/2022 were sent a questionnaire at 3 and 6 months. Outcomes included the Psychological Consequences Questionnaire (PCQ), the short-form of the Spielberger State-Trait Anxiety Inventory, and the EuroQoL five Dimensions five Levels scale (EQ-5D-5L). Data were analysed using regression adjusting for participant age, sex, socioeconomic status, education, baseline quality of life (EQ-5D-5L), and ethnicity., Results: A total of 380 (76%) participants returned questionnaires at 3 months and 328 (66%) at 6 months. There was no difference in any outcomes between participants with a normal scan and those with abnormal scans requiring no further action. Individuals requiring initial further investigations or referral had higher scores on the negative PCQ than those with normal scans at 3 months (standardised mean difference 0.28 sd, 95% confidence interval 0.01-0.54; P = 0.044). The difference was greater in those with anxiety or depression at baseline. No differences were seen at 6 months., Conclusion: Screening for kidney cancer and other abdominal malignancies using abdominal CT alongside the thoracic CT within lung cancer screening is unlikely to cause significant lasting psychosocial or financial harm to participants with incidental findings., (© 2023 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Objective: To systematically identify and compare the performance of prognostic models providing estimates of survival or recurrence of localized renal cell cancer (RCC) in patients treated with surgery with curative intent., Materials and Methods: We performed a systematic review (PROSPERO CRD42019162349). We searched Medline, EMBASE and the Cochrane Library from 1 January 2000 to 12 December 2019 to identify studies reporting the performance of one or more prognostic model(s) that predict recurrence-free survival (RFS), cancer-specific survival (CSS) or overall survival (OS) in patients who have undergone surgical resection for localized RCC. For each outcome we summarized the discrimination of each model using the C-statistic and performed multivariate random-effects meta-analysis of the logit transformed C-statistic to rank the models., Results: Of a total of 13 549 articles, 57 included data on the performance of 22 models in external populations. C-statistics ranged from 0.59 to 0.90. Several risk models were assessed in two or more external populations and had similarly high discriminative performance. For RFS, these were the Sorbellini, Karakiewicz, Leibovich and Kattan models, with the UCLA Integrated Staging System model also having similar performance in European/US populations. All had C-statistics ≥0.75 in at least half of the validations. For CSS, they the models with the highest discriminative performance in two or more external validation studies were the Zisman, Stage, Size, Grade and Necrosis (SSIGN), Karakiewicz, Leibovich and Sorbellini models (C-statistic ≥0.80 in at least half of the validations), and for OS they were the Leibovich, Karakiewicz, Sorbellini and SSIGN models. For all outcomes, the models based on clinical features at presentation alone (Cindolo and Yaycioglu) had consistently lower discrimination. Estimates of model calibration were only infrequently included but most underestimated survival., Conclusion: Several models had good discriminative ability, with there being no single 'best' model. The choice from these models for each setting should be informed by both the comparative performance and availability of factors included in the models. All would need recalibration if used to provide absolute survival estimates., (© 2022 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Objective: To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models., Methods: Risk models were identified from a recent systematic review and the Cancer-PRS web directory. A narrative synthesis of the models, previous validation studies and related genome-wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925)., Results: A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic-only models (seven of 25) and most of the mixed genetic-phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers., Conclusions: Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined., (© 2022 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Objectives: To externally validate risk models for the detection of kidney cancer, as early detection of kidney cancer improves survival and stratifying the population using risk models could enable an individually tailored screening programme., Methods: We validated the performance of 30 existing phenotypic models predicting the risk of kidney cancer in the UK Biobank cohort (n = 450 687). We compared the discrimination and calibration of models for men, women, and a mixed-sex cohort. Population level data were used to estimate model performance in a screening scenario for a range of risk thresholds (6-year risk: 0.1-1.0%)., Results: In all, 10 models had reasonable discrimination (area under the receiver-operating characteristic curve >0.60), although some had poor calibration. Modelling demonstrated similar performance of the best models over a range of thresholds. The models showed an improvement in ability to identify cases compared to age- and sex-based screening. All the models performed less well in women than men., Conclusions: The present study is the first comprehensive external validation of risk models for kidney cancer. The best-performing models are better at identifying individuals at high risk of kidney cancer than age and sex alone; however, the benefits are relatively small. Feasibility studies are required to determine applicability to a screening programme., (© 2021 The Authors. BJU International published by John Wiley & Sons Ltd on behalf of BJU International.)

Objectives: To review urinary protein biomarkers as potential non-invasive, easily obtainable, early diagnostic tools in renal cell carcinoma (RCC)., Methods: A PubMed database search was performed up to the year 2020 to identify primary studies reporting potential urinary protein biomarkers for RCC. Separate searches were conducted to identify studies describing appropriate methods of developing cancer screening programmes and detection of cancer biomarkers., Results: Several urinary protein biomarkers are under validation for RCC diagnostics, e.g. aquaporin-1, perilipin-2, carbonic anhydrase-9, Raf-kinase inhibitory protein, nuclear matrix protein-22, 14-3-3 Protein β/α and neutrophil gelatinase-associated lipocalin. However, none has yet been validated or approved for clinical use due to low sensitivity or specificity, inconsistencies in appropriate study design, or lack of external validation., Conclusions: Evaluation of biomarkers' feasibility, sample preparation and storage, biomarker validation, and the application of novel technologies may provide a solution that maximises the potential for a truly non-invasive biomarker in early RCC diagnostics., (© 2021 The Authors BJU International © 2021 BJU International Published by John Wiley & Sons Ltd.)