Researchers at the Australian National University (ANU) have made a remarkable advancement in personalized cancer treatment with a new predictive AI tool.
According to research findings published in the journal Nature Cancer, researchers have developed an AI-powered tool that can determine the most suitable treatment pathway for cancer patients based on their messenger RNA profile.
Known as DeepPT, the tool was developed in collaboration with researchers from the pharmaceutical company Pangea Biomed and the National Cancer Institute in America to help predict patients’ messenger RNA (mRNA) profiles for personalized cancer treatment.
The mRNA is an essential chemical in protein production and a critical piece of molecular information for personalizing cancer therapy.
How DeePT Works for Personalized Cancer Treatment
According to Dr Danh-Tai Hoang, the lead author from ANU, DeePT proved successful in predicting patients’ responses to cancer therapy across different types of cancer when used with another AI-powered tool, ENLIGHT.
By selecting the best cancer treatment for patients, healthcare professionals can improve the overall treatment outcomes. That’s what makes this kind of research transformational.
DeePT is trained on a vast number of datasets drawn from over 5,000 patients suffering from 16 different cancer types, including lung, breast, cervical, pancreatic, neck, and head cancers. As a result, the AI tool has great predictive power that enables it to analyze diverse microscopic images of patient tissues and predict a potential response to cancer therapy.
By analyzing histopathology images, DeePT gives timely insights, cutting down delays associated with processing complex molecular data, which usually take weeks. Additionally, histopathology images are cost-effective and routinely available when needed.
The findings highlight the potential of artificial intelligence to enhance cancer therapy outcomes, with tools such as DeePT and ENLIGHT opening a new avenue for personalized cancer treatment selections that can improve patients’ overall therapy outcomes.
