Researchers from Brigham and Women’s Hospital, a founding member of the Brigham General Mass. health system, have used artificial intelligence tools to accelerate understanding of the risk of specific cardiac arrhythmias when different parts of the heart are exposed to different thresholds of radiation as part of a lung cancer treatment plan. Their findings are published in JACC: CardioOncology.
Radiotherapy equipment. Image credit: Government of the State of São Paulo via FlickrCC PAR 2.0
“Radiation exposure to the heart during lung cancer treatment can have very serious and immediate effects on a patient’s cardiovascular health,” said corresponding author Raymond Mak, MD, of the Department of Radiation Oncology at Brigham and Women’s Hospital. “We hope to educate not only oncologists and cardiologists, but also patients receiving radiation therapy, about the risks to the heart when treating lung cancer tumors with radiation therapy.”
The emergence of artificial intelligence tools in healthcare has been revolutionary and has the potential to positively reshape the continuum of care, including informing treatment plans for cancer patients. Mass General Brigham, as one of the nation’s top integrated academic health systems and largest innovation companies, is leading the way by conducting rigorous research on new and emerging technologies to inform the responsible incorporation of AI into care delivery.
In patients receiving radiation therapy for non-small cell lung cancer (NSCLC), arrhythmias, or irregular heart rhythms, can be common. Because the heart is so close to the lungs and because NSCLC tumors are located near or around the heart, the heart can suffer collateral damage from the radiation dose that is intended to target the cancerous tumors. Previous studies have shown that this type of exposure to the heart is associated with general heart problems. However, this nuanced study demonstrated that the risk of different types of arrhythmias can vary considerably depending on the pathophysiology and the heart structures exposed to different levels of radiation.
To classify the types of arrhythmias associated with the cardiac substructures receiving radiation, the researchers conducted a retrospective analysis of 748 patients in Massachusetts treated with radiation therapy for locally advanced NSCLC. The arrhythmia subtypes cataloged included atrial fibrillation, atrial flutter, other supraventricular tachycardias, bradyarrhythmia, and ventricular tachyarrhythmia or asystole.
The team’s statistical analyses indicated that about one in six patients experienced at least one grade 3 arrhythmia with a median time to first arrhythmia of 2.0 years. Grade 3 classifications are considered serious events likely requiring intervention or hospitalization. They also found that nearly a third of patients with arrhythmias also suffered major adverse cardiac events.
The arrhythmia classes described in the study did not fully encompass the range of possible heart rhythm problems, but the authors note that these observations nevertheless provide insight into possible pathophysiological pathways and potential avenues for minimizing cardiac toxicity after receiving radiation therapy. Their work also provides a predictive model for the exposure dose and type of arrhythmia expected.
Looking ahead, the researchers believe that radiation oncologists should collaborate with cardiology experts to better understand the mechanisms of heart damage and their relationship to radiation therapy. In addition, they should take advantage of modern radiation therapy to actively reduce radiation exposure to specific areas of the heart that are at high risk of causing arrhythmias. Mak says this study, along with previous research, will contribute to monitoring, screening, and informing radiation oncologists about which parts of the heart to limit radiation exposure to and, therefore, mitigate complications.
“An interesting part of what we did was leverage AI algorithms to segment structures like the pulmonary vein and parts of the conduction system to measure radiation dose exposure in over 700 patients. This saved us many months of manual work,” Mak said. “So not only does this work have potential clinical impact, it also opens the door to using AI in radiation oncology research to streamline discovery and create larger datasets.”
Source: BWH
Originally published in The European Times.
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