Radiation therapy

The study find that the New Cleveland Clinic-led research shows that artificial intelligence (AI) can use medical scans and health records to personalize the dose of radiation therapy used to treat cancer patients. Published today in The Lancet Digital Health, Therefore the research team develop an AI framework base on patient computerize tomography (CT) ;scans and electronic health records.

Dose of radiation therapy

This new AI framework is the first to use medical scans to inform radiation dosage; moving the field forward from using generic dose prescriptions to more individualize treatments. Currently, radiation therapy is deliver uniformly. The dose deliver does not reflect differences in individual tumor characteristics or patient-specific factors that may affect treatment success.

The AI framework begins to account for this variability and provides individualized radiation doses that can reduce the treatment failure probability to less than 5%. Therefor “While highly effective in many clinical settings, radiotherapy can greatly benefit from dose optimization capabilities;” says lead author Mohamed Abazeed; M.D., Ph.D., a radiation oncologist at Cleveland Clinic’s Taussig Cancer Institute and a researcher at the Lerner Research Institute.

“This framework will help physicians develop data-driven; personalize dosage schedules that can maximize the likelihood of treatment success and mitigate radiation side effects for patients.” The framework is built using CT scans and the electronic health records of 944 lung cancer patients treat with high-dose radiation.

Provides individualized radiation

Pre-treatment scans are input into a deep-learning model; which analyze the scans to create an image signature that predicts treatment outcomes. Therefore Using sophisticate mathematical modeling; this image signature is combine with data from patient health records which describe clinical risk factors to generate a personalize radiation dose.

“The development and validation of this image-based; deep-learning framework is exciting because not only is it the first to use medical images to inform radiation dose prescriptions; but it also has the potential to directly impact patient care;” said Dr. Abazeed. Because “The framework  ultimately be use to deliver radiation therapy tailor to individual patients in everyday clinical practices.”

There are several other factors that set this first-of-its-kind framework apart from other similar clinical machine learning algorithms and approaches. Because The technology develop by the team uses an artificial neural network that merges classical approaches of machine learning with the power of a modern neural network.

Similar clinical machine

The network determines how much prior knowledge to use to guide predictions about treatment failure. But The extent that prior knowledge informs the model is tunable by the network. Therefore This hybrid approach is ideal for clinical applications since most clinical datasets in individual hospitals are more modest in sample size compared to non-clinical datasets used to make other well-known AI predictions (i.e. online shopping or ride-sharing).