Artificial intelligence (AI) tools may be used to better understand the risk of specific cardiac arrhythmias when various parts of the heart are exposed to different thresholds of radiation as part of a treatment plan for non–small cell lung cancer (NSCLC), according to a recent study published by Atkins et al in JACC: CardioOncology.
Background
Cardiac arrhythmias are common among patients receiving radiation therapy to treat NSCLC. Because of the close proximity of the heart to the lungs and with NSCLC tumors being near or around the heart, the heart can receive collateral damage from radiation dose spillage meant to target the tumors. Prior research has found that this type of exposure to the heart can be associated with general cardiac issues.
“Radiation exposure to the heart during lung cancer treatment can have very serious and immediate effects on a patient’s cardiovascular health,” stressed senior study author Raymond Mak, MD, of the Department of Radiation Oncology at Brigham and Women’s Hospital. “We are hoping to inform not only oncologists and cardiologists, but also patients receiving radiation treatment, about the risks to the heart when treating lung cancer tumors with radiation,” he added.
The emergence of AI tools in health care has been groundbreaking, demonstrating its potential to positively reshape the continuum of care—including informing treatment plans in patients with cancer.
Study Methods and Results
In the recent study, investigators conducted a retrospective analysis of 748 patients with locally advanced NSCLC who were treated with radiation—with the goal of classifying the types of cardiac arrhythmias that are associated with cardiac substructures receiving radiation. The subtypes of cardiac arrhythmias included atrial fibrillation, atrial flutter, other supraventricular tachycardia, bradyarrhythmia, and ventricular tachyarrhythmia or asystole. The investigators demonstrated that the risk for different types of cardiac arrhythmias may vary based on the pathophysiology and cardiac structures that are exposed to different levels of radiation.
Among the patients involved in the study, about 17% of them experienced at least one grade 3 cardiac arrhythmia—considered serious and likely requiring intervention or hospitalization—with a median time of 2 years until the first cardiac arrhythmia. The investigators also discovered that nearly 33% of the patients with cardiac arrhythmias also experienced major adverse cardiac events.
Conclusions
The arrhythmia classes outlined in the study did not entirely encompass the range of heart rhythm issues possible. Nonetheless, the investigators noted that their observations may advance the understanding of the potential pathophysiology pathways and avenues for minimizing cardiac toxicity following receipt of radiation therapy. The findings may offer a predictive model for dose exposure and the type of expected cardiac arrhythmia.
The investigators proposed that radiation oncologists should collaborate with cardiology specialists to uncover the mechanisms of heart injuries and their connection to radiation therapy. In addition, physicians should take advantage of modern radiation therapy to actively sculpt radiation exposure away from the specific cardiac regions at high risk of causing cardiac arrhythmias.
The investigators hope their research will help with surveillance, screening, and informing radiation oncologists about which parts of the heart should receive limited radiation exposure in order to 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 the radiation dose exposure in over 700 patients. This saved us many months of manual work,” emphasized Dr. Mak. “So, not only does this work have potential clinical impact, but it also opens the door for using AI in radiation oncology research to streamline discovery and create larger data sets,” he concluded.
Disclosure: For full disclosures of the study authors, visit jacc.org.
