Early cancer detection improves chances of cure and requires less intensive treatment, but screening programs exist only for few cancers and not everyone takes advantage. Researchers developed a model using Danish health data to predict individual risks for 20 different cancers based on previous diagnoses, family history, age, and risk factors. The model achieved 81% accuracy over a lifetime and 59% considering age and gender, with highest accuracy for digestive, thyroid, kidney, and uterine cancers. While not providing exact predictions, the model enables risk stratification to offer further screening to high-risk individuals. However, comprehensive linked health data is essential for such models, underscoring the need for national digital health infrastructures.
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Predicting Individual Cancer Risk
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