Researchers Capture First Images of Oxygen in Cancer Tumors during Radiation Therapy

Oxygen in cancer tumors is known to be a major factor that helps radiation therapy be successful. Hypoxia, or starvation of oxygen, in solid tumors is also thought to be an important factor in resistance to therapy. However, it is difficult to monitor tumor oxygenation without invasive sampling of oxygen distributions throughout the tissue, or without averaging across the whole tumor, whereas oxygen is highly heterogenous within a tumor. A research team at Dartmouth's and Dartmouth-Hitchcock's Norris Cotton Cancer Center led by Brian Pogue, PhD, has developed the first non-invasive way to directly monitor oxygen distributions within the tumor right at the time when radiation therapy is happening. With injection of an oxygen probe drug, PtG4, they are able to image the distribution of oxygen from within the tumor. The method measures the luminescence lifetimes of PtG4 while it is excited by the Cherenkov light emitted by the radiation therapy. The drug, PtG4, stays in the tumor for at least a week, and works for imaging repeatedly.

"The imaging is all done without any additional radiation, simply by using a camera to monitor the emissions during radiotherapy treatment," explains Pogue. "Following two tumor lines, one which is known to be responsive to radiation and one which is known to be resistant, we could see differences in the oxygenation of the tumor which are reflective of their differences in response." The team's findings, "Tissue pO2 Distributions in Xenograft Tumors Dynamically Imaged by Cherenkov-Excited Phosphorescence during Fractionated Radiation Therapy," are newly published in Nature Communications, by lead author, Xu Cao.

Pogue's team is able to capture oxygenation imaging through special technology. "We have a unique set of time-gated cameras in our radiation therapy department that were designed for Cherenkov-based radiation dosimetry, but we have used them for this additional purpose of monitoring oxygen in the tumors under treatment," says Pogue. "So access to these specialized Cherenkov cameras made the measurements possible." Pogue's team also collaborated with Professor Sergei Vinogradov and his team at the University of Pennsylvania Perelman School of Medicine, who produced the PtG4 and supported the work with drug characterization and co-supervision of the study.

Pogue hopes to develop this tumor monitoring ability into a useful clinical aid used to track tumor response to radiation therapy, especially tumors that are known to be hypoxic. Having such information available at the time of treatment could be helpful in influencing treatment decisions such as giving a radiation boost where needed. "When a patient gets radiation therapy, the treatment should be designed to directly utilize as much information about the patient's tumor as possible," says Pogue. "Today, we use the shape of the tumor and the tissue around it. But, we need to also think about using measurements of the tumor metabolism because this affects the success of treatment as well. Future radiation therapy treatments should ideally incorporate metabolic features such as oxygenation of the tumor when the treatment is planned or delivered."

The next steps toward this future are already underway. Pogue's team is looking to characterize how small of a region they can track the oxygenation from, and how fast they can take measurements. "Our goal is to produce oxygen images at video rate, with a spatial resolution that allows us to see radiobiologically relevant hypoxia nodules in the tumor of humans," explains Pogue.

Xu Cao, Srinivasa Rao Allu, Shudong Jiang, Mengyu Jia, Jason R Gunn, Cuiping Yao, Ethan P LaRochelle, Jennifer R Shell, Petr Bruza, David J Gladstone, Lesley A Jarvis, Jie Tian, Sergei A Vinogradov, Brian W Pogue.
Tissue pO2 distributions in xenograft tumors dynamically imaged by Cherenkov-excited phosphorescence during fractionated radiation therapy.
Nature Communications volume 11, Article number: 573, 2020. doi: 10.1038/s41467-020-14415-9.

Most Popular Now

AI Tool Beats Humans at Detecting Parasi…

Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose...

Do Fitness Apps do More Harm than Good?

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may...

Making Cancer Vaccines More Personal

In a new study, University of Arizona researchers created a model for cutaneous squamous cell carcinoma, a type of skin cancer, and identified two mutated tumor proteins, or neoantigens, that...

A New AI Model Improves the Prediction o…

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the...

AI, Health, and Health Care Today and To…

Artificial intelligence (AI) carries promise and uncertainty for clinicians, patients, and health systems. This JAMA Summit Report presents expert perspectives on the opportunities, risks, and challenges of AI in health...

AI can Better Predict Future Risk for He…

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack. The study, conducted by an international...

AI System Finds Crucial Clues for Diagno…

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly...

Improved Cough-Detection Tech can Help w…

Researchers have improved the ability of wearable health devices to accurately detect when a patient is coughing, making it easier to monitor chronic health conditions and predict health risks such...

Multimodal AI Poised to Revolutionize Ca…

Although artificial intelligence (AI) has already shown promise in cardiovascular medicine, most existing tools analyze only one type of data - such as electrocardiograms or cardiac images - limiting their...

New AI Tool Makes Medical Imaging Proces…

When doctors analyze a medical scan of an organ or area in the body, each part of the image has to be assigned an anatomical label. If the brain is...