Risk Prediction Models for Kidney Cancer: A Systematic Review
Context Early detection of kidney cancer improves survival; however, low prevalence means that population-wide screening may be inefficient. Stratifying the population into risk categories could allow for the introduction of a screening program tailored to individuals. Objective This review will identify and compare published models that predict the risk of developing kidney cancer in the general population. Evidence Acquisition A 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 TRIPOD guidelines and evaluated using the PROBAST assessment tool.
Evidence Synthesis The 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 BMI. Most of the models had acceptable-to-good discrimination (AUROC>0.7) in development and validation. Many 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.
Conclusion We identified a small number of risk models that could be used to stratify the population according to risk of kidney cancer. Most exhibit reasonable discrimination but few have been externally validated in population-based studies. Patient Summary In 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 individual risk. Most of the models identified require further testing in the general population to confirm their usefulness.
Cancer Research UK (21464)
Medical Research Council (MC_UU_12015/4)