The impact of deploying Artificial Intelligence (AI) for radiation cancer therapy in a real-world clinical setting has been tested by Princess Margaret researchers in a unique study involving physicians and their patients. A team of researchers directly compared physician evaluations of radiation treatments generated by an AI machine learning (ML) algorithm to conventional radiation treatments generated by humans. They found that in the majority of the 100 patients studied, treatments generated using ML were deemed to be clinically acceptable for patient treatments by physicians. Overall, 89% of ML-generated treatments were considered clinically acceptable for treatments, and 72% were selected over human-generated treatments in head-to-head comparisons to conventional human-generated treatments. Moreover, the ML radiation treatment process was faster than the conventional human-driven process by 60%, reducing the overall time from 118 hours to 47 hours.

The impact of deploying Artificial Intelligence (AI) for radiation cancer therapy in a real-world clinical setting has been tested by Princess Margaret researchers in a unique study involving physicians and their patients. A team of researchers directly compared physician evaluations of radiation treatments generated by an AI machine learning (ML) algorithm to conventional radiation treatments generated by humans. They found that in the majority of the 100 patients studied, treatments generated using ML were deemed to be clinically acceptable for patient treatments by physicians. Overall, 89% of ML-generated treatments were considered clinically acceptable for treatments, and 72% were selected over human-generated treatments in head-to-head comparisons to conventional human-generated treatments. Moreover, the ML radiation treatment process was faster than the conventional human-driven process by 60%, reducing the overall time from 118 hours to 47 hours.

Cancer care teams have faced unprecedented pressure over 2020. But out of these challenges have come important learnings about how we rethink cancer care for the future. Cancer Research UK anticipates that there will be 514,000 new cancer cases per year by 2035, an increase of more than 40%. These are significant numbers and we can not underestimate the pressure this will put on healthcare teams. Especially given that we are experiencing international shortages across all key cancer care professionals from radiation oncologists to specialist cancer nurses. Rising cases, combined with a shortage of skilled staff, could lead to a dangerous crisis point for cancer care.

The identification of predictive biomarkers from a large scale of covariates for subgroup analysis has attracted fundamental attention in medical research. In this article, we propose a generalized penalized regression method with a novel penalty function, for enforcing the hierarchy structure between the prognostic and predictive effects, such that a nonzero predictive effect must induce its ancestor prognostic effects being nonzero in the model. Our method is able to select useful predictive biomarkers by yielding a sparse, interpretable, and predictable model for subgroup analysis, and can deal with different types of response variable such as continuous, categorical, and time-to-event data. We show that our method is asymptotically consistent under some regularized conditions. To minimize the generalized penalized regression model, we propose a novel integrative optimization algorithm by integrating the majorization-minimization and the alternating direction method of multipliers, which is named after \texttt{smog}. The enriched simulation study and real case study demonstrate that our method is very powerful for discovering the true predictive biomarkers and identifying subgroups of patients.

A 13-year-old boy from Oregon has won the Young Scientist Challenge by inventing an artificial intelligence treatment for pancreatic cancer. Rishab Jain created an algorithm to improve cancer treatment by using AI to locate and track the pancreas in real time. A prime challenge in radiation treatment is locating the pancreas itself, which is often obscured by the stomach or other organs, resulting in healthy cells being inadvertently hit. Rishab's algorithm improves accuracy and increases the impact of radiation treatment, according to organizers of the competition. The seventh grade student said he started the project last year, when he learned that pancreatic cancer, the third-leading cause of cancer deaths, is devastating and fast-growing.

While pregnant with her first child, Rhea Birusingh started experiencing blurry vision that her OB-GYN dismissed as an expected pregnancy-related change, but three months later, she went to her ophthalmologist, who discovered an inoperable 2-centimeter benign brain tumor behind her right eye. Now, nearly four months later, Birusingh's son is healthy and her vision is normal, thanks to a powerful, precise radiation treatment. "When you're a pathologist and your eyes are a money maker, you start to get a little bit worried," Birusingh, 37, of Howey-in-the-Hills, Florida, told FoxNews.com. Doctors at UF Health Cancer Center – Orlando Health decided to use the treatment, called proton beam therapy, because Birusingh's tumor was adjacent to her hippocampus, which is critical for short- and long-term memory and learning. Proton beam therapy works differently from conventional radiation treatments, which rely on X-rays.