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Accelerating personalised treatment

Analytics

24 September

Researchers in Sweden have produced new prediction models to accelerate personalised treatment of lymph nodes in breast cancer patients. 

Tiffany Hionas

Predictive modelling is a process that applies available data to determine susceptibility of a patient to a specific condition. Predictive models can be used to identify trends and to identify at-risk groups, equipping the health care industry and employers with action plans. 

Lisa Rydén, a professor of surgery at Lund University and consultant at Skåne University Hospital reveals that "it is well known that knowledge of the spread of breast cancer to the axillary lymph nodes provides important information on the course of the disease, and lymph nodes are routinely removed for investigation. Around 70 per cent of patients are found to have healthy lymph nodes, and surgery could be avoided if they could instead be assessed in a different way.” Thus, based on the historical data on a patient, predictive modelling can be utilised to estimate the spread of the disease to the lymph nodes.

Gene expressions from approximately 3000 breast tumours have been studied to investigate the link between the spread of disease to the lymph nodes. Results demonstrated that the size of tumour and the invasion of cancer cells into vessels were significant in estimating the likelihood of disease outcome. 

A prediction model based on the tumour’s genetic profile and a collection of data on tumour characteristics, identified 6-7 per cent more women with healthy lymph nodes in patients with hormone-sensitive breast tumour, than other models. 

Artificial neural networks used for predictive analysis, can process the past and current data to estimate the probability of risk and therefore, to mitigate the spread of disease. Essentially, it can reduce surgical interventions by 30 percent. 

As Lisa Ryden concludes, ‘the results indicate that we may be a step closer to more personalised surgical treatment by using the prediction models as a decision support tool.’

DOI: /10.1158/1078-0432.CCR-19-0075 

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