Conference Coverage

How AI is, or will soon be, relevant in radiation oncology



Artificial intelligence (AI) is impacting many aspects of health care, and radiation oncology is no exception. It has the potential to cut costs and streamline work flows ranging from image analysis to treatment plan formulation, but its specific place in clinical practice is still being debated.

In a session at the annual meeting of the American Society for Radiation Oncology, researchers discussed some of the ways that AI is or will soon be relevant to the clinic. The general consensus was that AI provides a useful supplement but is no threat to replace the role of radiation oncologists.

In his talk, Sanjay Aneja, MD focused on practical applications of AI that are in the clinic or close to being ready. One example is image classification. “There has been recent evidence that suggests in a variety of different kind of scenarios, deep-learning models can be very good at image classification in automated ways,” said Dr. Aneja, who is a professor of radiology at Yale University, New Haven, Conn. He described one study that used AI to classify 14 different pathologies on chest x-ray images.

Dr. Aneja described the open-source nnU-net tool, which automatically configures itself and segments biomedical images for research or clinical purposes, including therapy planning support, intraoperative support, and tumor growth monitoring. The researchers who developed it also created a “recipe” to systematize configuration of nnU-net, making it useful as an out-of-the-box tool for image segmentation.

He predicted that AI will improve radiology oncology by assisting in the determination of disease extent, including microscopic areas of disease. It could also help plan treatment volume and monitor treatment response. “I think that these are the types of things that will be moving toward the clinic in the future; very specific applications and models trained on very specific scenarios that will help us answer a very important clinical question,” Dr. Aneja said.

He expects AI to contribute to auto-segmenting and clinical contouring, “but I will caution everyone that these algorithms have not been proven to be better than physician contours. They very frequently fail in the specific use cases when anatomy is distorted by, I don’t know, say a tumor. And so a lot of times, we don’t actually have the ability to just make it an automated process. I think it’ll be something that physicians will use to help them but not necessarily replace their contouring ability,” Dr. Aneja said.

Another, potentially more useful application, is in adaptive radiation planning. “I think that AI auto-contouring will be very helpful in establishing contours in a situation in which a physician doing them would not be feasible. We need to have nimble and computationally efficient auto segmentation algorithms that will be able to be easily deployed at the linear accelerator,” he said.

AI in pathology and treatment selection

In another talk, Osama Mohamad, MD talked about AI in pathology, and specifically treatment selection. He described research from his group that digitized pathology data from 5,500 patients drawn from five randomized, clinical trials. They used AI on data from four of the clinical trials to identify a prognostic biomarker for distant metastasis, then validated it on data from the remaining clinical trial, which compared radiation versus radiation plus short-term hormone therapy in prostate cancer.


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