(CTN News) – According to a recent study conducted by Northwestern Breast CancerMedicine, a new artificial intelligence (AI) tool has the potential to revolutionize breast cancer treatment.
This tool offers a more accurate method of predicting patient outcomes, allowing for the possibility of sparing patients unnecessary chemotherapy treatments.
In fact, the AI evaluations of patient tissues outperformed evaluations conducted by expert pathologists in terms of predicting the future course of the disease.
By utilizing this AI tool, it becomes possible to identify breast cancer patients who are currently classified as high or intermediate risk but have the potential to become long-term survivors.
As a result, the duration or intensity of their chemotherapy could be significantly reduced. This development is particularly significant as chemotherapy often leads to unpleasant and harmful side effects, such as nausea or, in rare cases, damage to the heart.
The assessment of cancerous cells in a patient’s tissue by pathologists is currently used to determine the appropriate treatment. However, a recent study has revealed that the patterns of non-cancerous cells play a crucial role in predicting outcomes.
This study is the first to utilize artificial intelligence (AI) for a comprehensive evaluation of both cancerous and non-cancerous components in invasive breast cancer.
According to Lee Cooper, the corresponding author of the study and an associate professor of pathology at Northwestern University Feinberg School of Medicine, this research highlights the significance of non-cancerous elements in determining a patient’s prognosis.
While the importance of these elements has been recognized through biological studies, their effective translation into clinical practice has been lacking.
By 2023, around 300,000 US women will be diagnosed with invasive breast cancer,
Highlighting its prevalence. Pathologists grade cancerous tissue to determine treatment, but research shows non-cancerous cells also play a significant role in cancer growth and inhibition. Understanding their role is crucial for effective treatment strategies.
Cooper and his team developed an AI system that assesses breast cancer tissue from digital images by analyzing the characteristics of cancerous and non-cancerous cells, as well as their interactions.
According to Cooper, these patterns can be difficult for pathologists to evaluate accurately, as they are often challenging for the human eye to categorize consistently.
The AI model, on the other hand, provides clear decision-making information to the pathologist by measuring these patterns. Cooper is also a member of the Robert H. Lurie Comprehensive Cancer Center of Northwestern University.