Background: Breast density, as derived from mammographic images and defined by the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS), is one of the strongest risk factors for breast cancer. Breast ultrasound (BUS) is an alternative breast cancer screening modality, particularly useful for early detection in low-resource, rural contexts. The purpose of this study was to explore an artificial intelligence (AI) model to predict BI-RADS mammographic breast density category from clinical, handheld BUS imaging. Methods: All data are sourced from the Hawaii and Pacific Islands Mammography Registry. We compared deep learning methods from BUS imaging, as well as machine learning models from image statistics alone. The use of AI-derived BUS density as a risk factor for breast cancer was then compared to clinical BI-RADS breast density while adjusting for age. The BUS data were split by individual into 70/20/10% groups for training, validation, and testing. Results: 405,120 clinical BUS images from 14.066 women were selected for inclusion in this study, resulting in 9.846 women for training (302,574 images), 2,813 for validation (11,223 images), and 1,406 for testing (4,042 images). On the held-out testing set, the strongest AI model achieves AUROC 0.854 predicting BI-RADS mammographic breast density from BUS imaging and outperforms all shallow machine learning methods based on image statistics. In cancer risk prediction, age-adjusted AI BUS breast density predicted 5-year breast cancer risk with 0.633 AUROC, as compared to 0.637 AUROC from age-adjusted clinical breast density. Conclusions: BI-RADS mammographic breast density can be estimated from BUS imaging with high accuracy using a deep learning model. Furthermore, we demonstrate that AI-derived BUS breast density is predictive of 5-year breast cancer risk in our population.

In March 2022 the Mayo Clinic Platform announced their new accelerator program for AI powered health tech startups. The 20 week immersive Mayo Clinic Platform Accelerate Program is designed to help innovative startups establish credibility and get market ready so that they can spark innovation in healthcare. Each company receives a $200,000 benefit package that includes access to Mayo Clinic's rich de-identified data sets, validation frameworks, clinical workflow planning, and mentorship. Startups in the accelerator receive guidance on clinical, regulatory, technology, and business decisions. There are also opportunities for workflow integration and clinical collaboration for research trials, access thought leadership, and peer-reviewed publications.

This month the AI Precision Health Institute at the University of Hawaiʻi Cancer Center launched a new seminar series on applications of AI in cancer research and clinical practice. The first lecture in the series was presentated by Bardia Kohn PhD. on November 4, 2022. Dr. Kohn is Associate Professor in the Mechanical Engineering Department of the University of Hawaii at Manoa and Director of the Advanced Materials and Medical Instrument Laboratory (AMMI Lab). The focus of Dr. Kohn's group is to develop new medical robotic systems to make surgeries less invasive and more accurate. Dr. Kohn presented his research on the Application of Robotics and AI in Prostate Cancer Diagnostic and Treatment Methods.

Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

DALLAS – Sept. 23, 2021 – Researchers and data scientists at UT Southwestern Medical Center and The University of Texas MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

Researchers and data scientists at UT Southwestern Medical Center and MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

Regional Cancer Care Associates, based in New Jersey, has more than 20 locations throughout New Jersey, Connecticut, Maryland, Pennsylvania and the Washington area. Staff realized they needed a risk-stratified list of patients for COVID-19 vulnerability that nurses could manage through phone calls and by coordinating services with other providers. Because of staffing challenges, the list had to identify only the high-risk patients who staff needed to manage first, not the entire population or those patients who could wait a bit longer for nurse outreach. "Even though we already had an indigenous and independent scoring logic/mechanism for patient risk, this was mainly based on a combination of comorbidities that differentiated it from the usual scoring techniques," explained Lani M. Alison, vice president of quality and value transformation at RCCA. "Thus," she said, "there was a need to further stratify the risk patients for COVID-19 vulnerability and to establish a patient-centered assessment and outreach." On another note, staff observed challenges in assigning these patients and a defined patient roster to care coordination executives or support staff, which was hindering a patient-centric outreach approach, Alison added.

An AI neural network can accurately predict the prognosis of melanoma patients based on pre-treatment histology imaging data, shows research led by the NYU Grossman School of Medicine. Immune checkpoint inhibitors have revolutionized melanoma treatment, but only some tumors respond well to them and they can be quite toxic to patients. Having a more reliable way to predict who is most likely to respond to these therapies is therefore crucial. "An unmet need is the ability to accurately predict which tumors will respond to which therapy," says Iman Osman, M.D., a medical oncologist based at New York University (NYU) Grossman School of Medicine and NYU Langone's Perlmutter Cancer Center, who co-led the work. "This would enable personalized treatment strategies that maximize the potential for clinical benefit and minimize exposure to unnecessary toxicity." In collaboration with Aristotelis Tsirigos, Ph.D., professor in the Institute for Computational Medicine at NYU Grossman School of Medicine and member of NYU Langone's Perlmutter Cancer Center, Osman and team first trained an artificial neural network using pre-treatment histology images from 121 patients with metastatic melanoma.

Penn Medicine researchers used federated learning to train an algorithm to analyze magnetic resonance imaging scans of brain tumor patients to distinguish healthy brain tissue from cancerous regions. Researchers at the University of Pennsylvania's Perelman School of Medicine, in conjunction with the University of Texas MD Anderson Cancer Center, Washington University, and the Hillman Cancer Center at the University of Pittsburgh, have developed a machine learning method that can facilitate the sharing of patient data without compromising privacy. The model uses the federated learning approach that trains an algorithm across multiple decentralized devices or servers containing local data samples without exchanging them. The researchers found the approach to be successful in analyzing magnetic resonance imaging (MRI) scans and distinguishing between healthy brain tissue and cancerous regions. The model could allow doctors in hospitals worldwide to input their own patient brain scans, which would support the development of a concensus model that would be clinically useful.

PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.