This Project Grant award from the National Cancer Institute under the Cancer Detection and Diagnosis Research program (CFDA 93.394) provides $1,199,514 to develop a comprehensive and scalable multimodal AI model, termed "PRECISE", that fuses imaging and non-imaging data to enable early detection of pancreatic ductal adenocarcinoma (PDAC). The project aims to create deep learning models that can segment imaging biomarkers from abdominal CT scans and combine them with clinical data...
The National Cancer Institute (NCI) awarded a $680,433 Project Grant under CFDA 93.394 - Cancer Detection and Diagnosis Research to The Regents of the University of California, San Francisco (UCSF) to conduct a multi-year research project titled "Multimodality Spatial Analysis in Prostate Cancer to Improve Prognostic Estimation and Cast Light into the Black Box of Pathology Artificial Intelligence Algorithms." The goal of this research is to apply cutting-edge spatial proteogenomic...
This federal Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $572,490 to The Regents of the University of California, San Francisco (UCSF) to develop an AI-powered tool called PILLAR. PILLAR aims to accurately predict breast cancer risk using longitudinal multi-modal breast imaging data, including mammograms, tomosynthesis, and MRIs. The key objectives are to: 1) develop the PILLAR AI model to improve upon current clinical...
This $989,122 Project Grant award from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to evaluate the performance of four commercial mammography-based artificial intelligence (AI) algorithms for breast cancer risk prediction in diverse U.S. screening populations. The project will assess the accuracy and equity of these AI risk models compared to traditional clinical risk factor-based models, using data from the Breast Cancer...
The National Cancer Institute (NCI) awarded a $570,634 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the University of Connecticut to develop a robust, multimodal, and longitudinal artificial intelligence (AI) system to enhance breast cancer screening. The overarching objective is to optimize and personalize breast cancer screening by advancing AI algorithms that can transform clinical decision-making. The key products/services to be delivered...
This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $245,087 to the University of Delaware from June 1, 2025 to May 31, 2027. The primary objectives are to: Curate a comprehensive dataset of 300 public and 50 Memorial Sloan Kettering MRI scans, annotated with corresponding PIRADS radiology reports, to enable machine-readable extraction of radiologist reasoning processes. Develop a Prostate Vision Language Model...
This federal Project Grant award of $194,022, provided by 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 facilitate the transition of a physician-scientist at Massachusetts General Hospital (MGH) to independence as a translational oncologist. The goal is to use deep learning (DL) to analyze and integrate clinically acquired data, including...
This Project Grant award from the National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), provides $1,625,000.00 to Onc.ai, Inc. to develop and validate a radiomics-based multi-modal predictive model for metastatic non-small cell lung cancer patients treated with PD-1 immunotherapy. The key objectives are to: 1) Validate the predictive models in a multi-institutional prospective clinical study, 2) Evaluate the performance characteristics and...
This $435,827 Project Grant from the National Cancer Institute (NCI) under the CFDA 93.394 Cancer Detection and Diagnosis Research program supports research conducted by New York University (NYU) School of Medicine to develop deep learning methods for analyzing mass spectrometry imaging (MSI) data. The goal is to make MSI data more accessible to existing machine learning workflows by expanding the dimensionality of the data structure to treat each metabolite or lipid as an individual "color...
This federal Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) to the Sloan-Kettering Institute for Cancer Research provides $700,611 to conduct a randomized controlled trial (RCT) evaluating the use of artificial intelligence (AI) to assist dermatologists in diagnosing melanoma. The project aims to determine the potential benefits and barriers to adopting AI technology, as well as assess its impact on reducing the number of unnecessary...
This federal Project Grant award, funded by 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), supports the development of an AI-powered system to enhance the detection and tracking of metastatic colorectal cancer using computed tomography (CT) imaging. The $718,456 award, with a project period from July 1, 2025 to May 31, 2029, will enable researchers at the University of California, San Francisco to create a large-scale database of standardized imaging and radiology reports, and leverage advanced AI techniques like radiology-report supervision, data augmentation, and active learning to train their anatomy-aware vision-language AI systems. The goal is to develop an innovative solution that can improve the accuracy and efficiency of evaluating metastatic disease in colorectal cancer, enabling earlier detection of subtle changes while reducing the cognitive workload on radiologists.