Anant Madabhushi was ready for the next step in his career as a researcher and educator. He was already widely recognized as a pioneer in the emerging field of machine learning--specifically for medical imaging and computer-assisted diagnoses. He had authored more than 450 peer-reviewed publications and held over one hundred patents in AI, radiomics, computational pathology, and computer vision. He had even seen his name printed in major consumer publications such as Business Insider and Scientific American that spread the word about how algorithms he's created have greatly improved the accuracy of diagnosing cancer. But Madabhushi, a professor of biomedical engineering at Case Western Reserve University, wanted more. He wanted to break out of the lab and share his specialized knowledge of AI with doctors and clinicians who could put it to use in health care systems and hospitals.

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.

Electric vehicles have the potential to substantially reduce carbon emissions, but car companies are running out of materials to make batteries. One crucial component, nickel, is projected to cause supply shortages as early as the end of this year. Scientists recently discovered four new materials that could potentially help--and what may be even more intriguing is how they found these materials: the researchers relied on artificial intelligence to pick out useful chemicals test from a list of more than 300 options. And they are not the only humans turning to A.I. for scientific inspiration. Creating hypotheses has long been a purely human domain.

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.

New research at Case Western Reserve University could help better determine which patients diagnosed with the pre-malignant breast cancer commonly referred to as stage 0 are likely to progress to invasive breast cancer and therefore might benefit from additional therapy over and above surgery alone. Once a lumpectomy of breast tissue reveals this pre-cancerous tumor, most women have surgery to remove the remainder of the affected tissue and some are given radiation therapy as well, said Anant Madabhushi, the F. Alex Nason Professor II of Biomedical Engineering at the Case School of Engineering. "Current testing places patients in high risk, low risk and indeterminate risk--but then treats those indeterminates with radiation, anyway," said Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) conducted the new research. "They err on the side of caution, but we're saying that it appears that it should go the other way--the middle should be classified with the lower risk. "In short, we're probably overtreating patients," Madabhushi continued.

Researchers at Case Western Reserve University's digital imaging lab are pioneering the use of Artificial Intelligence (AI) to predict the efficacy of chemotherapy and determine which patients will benefit from the immunotherapeutic. This is especially true since roughly 20% of all cancer patients actually benefit from the immunotherapy-- a treatment that utilizes drugs to strengthen the immune system against the fight for cancer, as opposed to chemotherapy which are drugs that directly target the cancer cells. "This is no flash in the pan -- this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi. Technology has always been manipulated to advance healthcare. For this particular study, it's all about teaching a computer to detect the unseen changes in lung cancer CT scans and compare them to the first 2-3 cycles of immunotherapy treatment.

Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside -- and outside -- the tumor, a signature of the lab's recent research. "This is no flash in the pan -- this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

CLEVELAND–Scientists from the Case Western Reserve University digital imaging lab, already pioneering the use of Artificial Intelligence (AI) to predict whether chemotherapy will be successful, can now determine which lung-cancer patients will benefit from expensive immunotherapy. And, once again, they're doing it by teaching a computer to find previously unseen changes in patterns in CT scans taken when the lung cancer is first diagnosed compared to scans taken after the first 2-3 cycles of immunotherapy treatment. And, as with previous work, those changes have been discovered both inside–and outside–the tumor, a signature of the lab's recent research. "This is no flash in the pan–this research really seems to be reflecting something about the very biology of the disease, about which is the more aggressive phenotype, and that's information oncologists do not currently have," said Anant Madabhushi, whose Center for Computational Imaging and Personalized Diagnostics (CCIPD) has become a global leader in the detection, diagnosis and characterization of various cancers and other diseases by meshing medical imaging, machine learning and AI. Currently, only about 20% of all cancer patients will actually benefit from immunotherapy, a treatment that differs from chemotherapy in that it uses drugs to help your immune system fight cancer, while chemotherapy uses drugs to directly kill cancer cells, according to the National Cancer Institute.

Lung cancer is a common and often aggressive form of cancer. As it is difficult for doctors to detect it early on, people with lung cancer need to receive the best, most targeted therapy in order to make a positive outlook more likely. Immunotherapy is an option, but how can doctors know who will benefit? According to the National Cancer Institute, lung and bronchus cancer is the second most widespread type of cancer among people in the United States, accounting for 12.9% of all new cancer cases. This form of cancer often has no noticeable symptoms in its early stages, which can mean that doctors are unable to detect it at first.