Cooperative Agreement R44CA165436

The National Cancer Institute (NCI) awarded a $299,995 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the H. Lee Moffitt Cancer Center and Research Institute Hospital, Inc. in Tampa, FL. The grant will fund a project to develop and validate computed tomography (CT) image-based biomarkers that can predict survival outcomes for patients with high-grade serous ovarian carcinoma (HGSOC), the most common and deadly type of epithelial ovarian cancer. The...

This federal Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) provides $968,999 to the University of North Carolina at Chapel Hill to conduct a research study titled "OPTIMIZING SURVEILLANCE IN LUNG CANCER SURVIVORS WITH NOVEL IMAGING BIOMARKERS AND DEEP-LEARNING (OPTIMAL)". The goal of the study is to optimize post-treatment surveillance and monitoring of early-stage non-small cell lung cancer survivors by leveraging routine...

This federal Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), aims to improve the diagnostic accuracy and cost-effectiveness of breast cancer screening through the development of an AI system. The $672,087 grant, awarded on August 12, 2025, will fund a research project at New York University School of Medicine to build a...

This $1,686,258 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Onc.ai, Inc. aims to further develop and validate a deep learning radiomics biomarker for improved early response assessment in metastatic cancer therapy trials. The proposed approach uses deep learning models on CT scans at baseline and follow-up time points to generate a continuous "Serial CT Response Score" that can more accurately predict overall...

This federal Project Grant award, valued at $306,872 and provided by the National Cancer Institute under the Cancer Treatment Research program (CFDA 93.395), aims to develop a new imaging system termed the Multi-Camera Array Scanner (MCAS). The MCAS will enable rapid 3D digitization and automated analysis of thick fine-needle aspiration (FNA) cytology smears, which are a critical first step in the cancer diagnosis pipeline. The project is a collaboration between Ramona Optics Inc., a small...

The National Cancer Institute awarded Myolex, Inc. a $263,052 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to develop their proprietary MSCAN device leveraging Electrical Impedance Myography (EIM) technology. This project aims to apply EIM for the first time to detect and quantify cancer cachexia, a critical condition impacting half of all cancer patients. The MSCAN device provides a cost-effective, convenient alternative to traditional imaging techniques...

This Project Grant award, provided by the National Cancer Institute (CFDA 93.398 Cancer Research Manpower program), supports the development of an automated multimodal, multiscale algorithm for cervical cancer screening in low- and middle-income countries (LMICs). The $104,278 award, granted on January 11, 2026, will fund the following key objectives: Design a multimodal image registration algorithm to spatially correlate and combine information from widefield colposcope images and...

This Cooperative Agreement, awarded by the Department of the Interior's Interior Business Center under the Advanced Research Projects Agency for Health (ARPA-H) program (CFDA 93.384), provides $8,945,662.00 in funding to Emory University to develop a functional prototype system called "MARGINCALL" that can rapidly image surgical specimen contours, mark resection margins, and use AI to map cancer and assess tumor proximity to margins. The goal is to reduce current rates of lumpectomy...

This Project Grant award from the National Heart Lung and Blood Institute (CFDA 93.837 - Cardiovascular Diseases Research) provides $756,906 to Mayo Clinic to conduct a population-based study on using deep learning methods to detect and quantify breast arterial calcification (BAC) from mammogram images. The study aims to leverage the high rates of mammography screening to explore BAC as a biomarker for predicting cardiovascular disease risk in women. The project will analyze data from 125,519...

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Project Grant award (CFDA 47.070) of $195,000 will fund the development of a weakly-supervised breast cancer detection system using active and weakly-supervised learning techniques to improve breast cancer diagnosis from ultrasound images. The project aims to create a deep learning model that can detect breast cancer effectively with minimal annotations, addressing the challenges of noisy, low-contrast...