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Risk Prediction Models for Kidney Cancer: A Systematic Review.

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Identifying kidney cancer risk models can improve early detection. This review found several models using factors like age and smoking, but most need further validation in general populations.

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Area of Science:

  • Oncology
  • Epidemiology
  • Biostatistics

Background:

  • Early kidney cancer detection significantly improves survival rates.
  • Population-wide screening for kidney cancer is often inefficient due to low prevalence.
  • Risk stratification could enable tailored screening programs for individuals at higher risk.

Purpose of the Study:

  • To identify and compare existing models that predict the risk of developing kidney cancer in the general population.
  • To assess the performance and validation status of these kidney cancer risk prediction models.

Main Methods:

  • A systematic literature search was conducted in Medline and EMBASE to find studies reporting or validating kidney cancer risk models.
  • Studies were screened for inclusion, and data were extracted using a standardized form.
  • Risk models were classified using Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines and evaluated with the PROBAST tool.

Main Results:

  • Out of 15,281 identified articles, 62 met the inclusion criteria, with 11 models providing performance measures.
  • Commonly used risk factors included age, smoking status, and body mass index.
  • Most models demonstrated acceptable to good discrimination (AUC > 0.7), with some showing high specificity but low sensitivity. Biomarker-based models showed the highest performance but were developed in small studies.

Conclusions:

  • A limited number of kidney cancer risk models capable of population stratification were identified.
  • While most models show reasonable discrimination, only a few have undergone external validation in population-based studies.
  • Further testing of these models in the general population is recommended to confirm their clinical utility.