A Case Western Reserve University-led team of scientists has used artificial intelligence (AI) to identify which patients with certain head and neck cancers would benefit from reducing the intensity of treatments such as radiation therapy and chemotherapy. The researchers used AI tools similar to those they developed over the last decade at the Center for Computational Imaging and Personal Diagnostics (CCIPD) at Case Western Reserve. In this case, they asked the computer to analyze digitized images of tissue samples that had been taken from 439 patients from six hospital systems with a type of head and neck cancer known as human papillomavirus (HPV)-associated oropharyngeal squamous cell carcinoma (OPCSCC). The computer program successfully identified a subset of patients who might have benefited from a significantly reduced dose of radiation therapy. While that analysis was retrospective--meaning the computer analyzed data from patients in which the eventual outcome was already known--the researchers said their next step could be to test its accuracy in clinical trials.

A team of scientists and art historians at Case Western Reserve University say they have used tools of artificial intelligence (AI) to distinguish the individual brushstrokes of one painter from another. The technique could become a valuable tool to help authorities better identify forgeries of work by famous artists; it could also help art historians tell whether a master, or a student, contributed to a given masterpiece. The researchers said they believe the finding is among first of its kind because of how the researchers used the computer to read and learn from the 3D topography of a painting. Other forms of AI-enhanced analysis rely on visible stylistic differences that a program may detect in historic works, they said. The technology of 3D topography describes a three-dimensional relief map of a surface which reveals any differences in "elevation."

Scientists at Case Western Reserve University have used artificial intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment -- and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role -- using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.

Scientists at Case Western Reserve University have used Artificial Intelligence (AI) to identify new biomarkers for breast cancer that can predict whether the cancer will return after treatment--and which can be identified from routinely acquired tissue biopsy samples of early-stage breast cancer. The key to that initial determination is collagen, a common protein found throughout the body, including in breast tissue. Previous research had suggested that the collagen network, or arrangement of the fibers, relates strongly to breast cancer aggressiveness. But this work by Case Western Reserve researchers definitively demonstrated collagen's critical role--using only standard tissue biopsy slides and AI. The researchers, using machine-learning technology to analyze a dataset of digitized tissue samples from breast cancer patients, were able to prove that a well-ordered arrangement of collagen is a key prognostic biomarker for an aggressive tumor and a likely recurrence.

Computer programs have defeated humans in Jeopardy!, chess and Go. Now a program developed at Case Western Reserve University has outperformed physicians on a more serious matter. The program was nearly twice as accurate as two neuroradiologists in determining whether abnormal tissue seen on magnetic resonance images (MRI) were dead brain cells caused by radiation, called radiation necrosis, or if brain cancer had returned. The direct comparison is part of a feasibility study published in the American Journal of Neuroradiology today. "One of the biggest challenges with the evaluation of brain tumor treatment is distinguishing between the confounding effects of radiation and cancer recurrence," said Pallavi Tiwari, assistant professor of biomedical engineering at Case Western Reserve and leader of the study.