Abstract
ContextEarly detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratification of the population into risk categories could allow for the introduction of a screening programme tailored to individuals. ObjectiveThis review will identify and compare published models that predict the risk of developing kidney cancer in the general population. Evidence acquisitionA search identified primary research reporting or validating models predicting the risk of kidney cancer in Medline and EMBASE. After screening identified studies for inclusion, we extracted data onto a standardised form. The risk models were classified using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated using the PROBAST assessment tool. Evidence synthesisThe search identified 15 281 articles. Sixty-two satisfied the inclusion criteria; performance measures were provided for 11 models. Some models predicted the risk of prevalent undiagnosed disease and others future incident disease. Six of the models had been validated, two using external populations. The most commonly included risk factors were age, smoking status, and body mass index. Most of the models had acceptable-to-good discrimination (area under the receiver-operating curve >0.7) in development and validation. Many models also had high specificity; however, several had low sensitivity. The highest performance was seen for the models using only biomarkers to detect kidney cancer; however, these were developed and validated in small case-control studies. ConclusionsWe identified a small number of risk models that could be used to stratify the population according to the risk of kidney cancer. Most exhibit reasonable discrimination, but a few have been validated externally in population-based studies. Patient summaryIn this review, we looked at mathematical models predicting the likelihood of an individual developing kidney cancer. We found several suitable models, using a range of risk factors (such as age and smoking) to predict the risk for individuals. Most of the models identified require further testing in the general population to confirm their usefulness.
Highlights
Evidence acquisitionKidney cancer is the 15th most common cancer worldwide, with a significantly higher incidence in developed countries [1]
Detection and screening have been identified as priorities for kidney cancer research [5,6]
Early-stage diagnosis is strongly correlated with improved survival rates; the 5-yr cancer-specific survival rates for patients diagnosed with stage I and IV kidney cancer are 83% and 6%, respectively [7]
Summary
Kidney cancer is the 15th most common cancer worldwide, with a significantly higher incidence in developed countries [1]. A screening programme for highrisk individuals is hypothesised to increase early-stage detection and reduce mortality [12] This would reduce the burden of kidney cancer for both patients and healthcare systems [13]. The parameters of the screening approach, such as starting age and frequency of screening, could be tailored to the predicted level of risk for each individual This strategy requires a model that calculates the risk of developing kidney cancer for individuals. We aimed to systematically identify and compare published models that predict the risk of kidney cancer in the general population and describe the range of variables included, and the performance of the models and their potential applicability to population based stratification.
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