2023-04-23 15:00:31
However, a new advance in medical technology offers hope for patients with this disease. A group of researchers from the University of Oxford and the University of Nottingham have developed a new computer program which allows predict who is likely to develop lung cancerduring next ten years. This program, called CanPredict, uses a variety of measures, such as smoking, age, BMI and socioeconomic statusto calculate the risk of disease.
One of the main challenges in the fight against lung cancer is early detection. Screening is crucial for increase survival rates because the treatment is more effective when administered at an early stage of the disease. However, costs and staffing requirements to perform a computed tomography limit the scope of current screening.
Currently, doctors select people to screen using a survey that asks patients of a certain age about their smoking habits and their family history of the disease. Although useful, this method is not specific enough to identify all cases of lung cancer. CanPredict offers a more accurate solution by reviewing existing patient medical records to identify those at high risk.
The researchers have used data from 2.54 million anonymous medical records to determine who is most at risk for lung cancer. They then looked at which patients had actually developed the disease. CanPredict correctly identified more people who developed lung cancer than current methods. This suggests that this computer program could play a important role in the fight against this disease (source 1).
The advantage of CanPredict is that it can be used by general practitioners to identify patients at high risk of developing lung cancer, which could enable early intervention and potentially save lives.
“It works by looking at patients’ existing medical records, so it could be used automatically by GPs,” said Dr Weiqi Liao, lead author of the University of Oxford study.
Julia Hippisley-Cox, Professor of Clinical Epidemiology and General Medicine at the University of Oxford, said: “We hope this tool will help to better identify patients to screen for and catch lung cancer earlier, when treatments are more likely to be effective.”
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