Artificial intelligence advances cancer therapy
CLEVELAND, U.S.: In a recent advancement in cancer research, scientists have used artificial intelligence (AI) to help customize the radiation dosage for individual patient treatments. An earlier study showed that radiation therapy for throat cancer can produce better results than transoral robotic surgery (TORS). This latest development may help physicians in all fields prescribe better treatments and save more lives.
“While highly effective in many clinical settings, radiotherapy can greatly benefit from dose optimization capabilities,” explained lead author Dr. Mohamed Abazeed, 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, personalized dosage schedules that can maximize the likelihood of treatment success and mitigate radiation side effects for patients.”
After the recent increase in HPV infections and the doubling of cases of oropharyngeal cancer since the 1990s, treatment methods are becoming increasingly important. As reported by Dental Tribune International, results from the study investigating swallowing outcomes for throat cancer patients who underwent either TORS or radiation therapy challenged commonly held beliefs. This new study could potentially aid in producing even better outcomes.
“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 Abazeed. “The framework can ultimately be used to deliver radiation therapy tailored to individual patients in everyday clinical practices.”
According to the study, the AI system was constructed by using the information from 944 lung cancer patients treated with high-dose radiation, their CT scans and electronic health records. Pretreatment scans were entered into a deep-learning model, which analyzed the scans to create an image signature that predicted treatment outcomes. Using sophisticated mathematical modeling, this image signature was combined with data from patient health records—which describe clinical risk factors—to generate a personalized radiation dose.
Abazeed predicted that machine learning tools will play a larger role in the health care sector. He said that the image-based information platform not only can provide the ability to individualize multiple cancer therapies but is also is a leap forward in radiation precision medicine.
The study, titled “An image-based deep learning framework for individualising radiotherapy dose: A retrospective analysis of outcome prediction,” was published in the July 2019 issue of the Lancet Digital Health.