Project Grant R01CA304427
- Federal Grant Award Summary Wake Forest University Health Sciences received a $402,728 Project Grant from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394), awarded July 16, 2025, with completion targeted for June 30, 2027. The award supports development of interpretable deep learning models for multi-modality imaging-based gastric cancer prognosis. The project addresses the critical clinical need to improve prognostic accuracy and...
- Federal Grant Award Summary Wake Forest University Health Sciences received a $513,915 Project Grant award from the National Cancer Institute (NCI) on June 18, 2026, under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to develop and validate multimodal computational models for early prediction of peritoneal recurrence in gastric cancer. The project, which extends through May 31, 2031, leverages advanced deep learning techniques integrated with clinical, radiological (computed...
- Federal Project Grant Award Summary Wake Forest University Health Sciences received a $228,195 Project Grant 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), effective July 1, 2026, through June 30, 2028. The award funds the development and validation of multimodal predictive models designed to identify radiation failure patterns and predict survival...
- Federal Project Grant Award Summary The University of North Carolina at Chapel Hill received a $207,802 Project Grant from the National Cancer Institute under the Cancer Detection and Diagnosis Research program (CFDA 93.394) awarded May 1, 2026, with completion scheduled for April 30, 2029. The grant funds development of multiplexed multiscale imaging technologies and automated microscopy systems to advance cellular immunotherapy research, specifically targeting improved understanding of...
- Federal Grant Award Summary The National Cancer Institute (NCI) awarded $140,400 to Sloan-Kettering Institute for Cancer Research under the Cancer Research Manpower program (CFDA 93.398) to develop artificial intelligence (AI)-based methods that integrate genomic and histopathologic data to advance precision cancer medicine. This Project Grant, awarded July 17, 2026, and scheduled for completion by June 30, 2028, funds research focused on creating multimodal, interpretable AI models that combine...
- Federal Grant Award Summary The National Cancer Institute awarded a $825,627 Project Grant (CFDA 93.393: Cancer Cause and Prevention Research) to the University of California, Los Angeles, effective May 1, 2026 through April 30, 2031. The award funds development of advanced artificial intelligence models to improve breast cancer prognosis by integrating whole slide image (WSI) pathology data with clinical prognostic factors. The research deliverables include: (1) benchmark models for...
- Federal Project Grant Award Summary Case Western Reserve University received a $147,486 Project Grant from the National Cancer Institute (NCI) under the Cancer Research Manpower program (CFDA 93.398), awarded December 1, 2025, with completion targeted for May 31, 2027. The award supports the development of a pathologically interpretable computational imaging predictor to identify rectal cancer patients exhibiting complete response (CR) to total neoadjuvant therapy (TNT) who are candidates for...
- Federal Grant Award Summary The National Cancer Institute (NCI), under its Cancer Detection and Diagnosis Research program (CFDA 93.394), awarded a $640,956 Project Grant to the University of California, Los Angeles on May 15, 2026, with a completion date of April 30, 2031. This grant funds the development of an artificial intelligence (AI)-enhanced perfusion magnetic resonance imaging (MRI) tool designed to optimize yttrium-90 (Y90) microsphere distribution during radioembolization treatment of...
- Federal Grant Award Summary Duke University's Office of Research Administration received a $100,228 Project Grant from the National Cancer Institute (NCI) under the Cancer Research Manpower program (CFDA 93.398), effective December 1, 2026, through April 30, 2028. The award funds research and development of a verifiable and robust deformable image registration (DIR) method designed to improve precision tracking of diffuse glioma progression, particularly glioblastoma (GBM). The project addresses...
- Federal Grant Award Summary The National Cancer Institute (NCI) awarded $421,685 to The Regents of the University of California, San Francisco on June 1, 2026, under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to develop hyperpolarized 13C magnetic resonance imaging (HP 13C MRI) as a predictive biomarker for immunotherapy response in cancer patients. The two-year project, concluding May 31, 2028, addresses a critical clinical need by creating a non-radioactive, quantitative...
Wake Forest University Health Sciences received a $526,939 Project Grant award from the National Cancer Institute (NCI) on July 10, 2026, under the Cancer Detection and Diagnosis Research program (CFDA 93.394). The award funds research through June 30, 2031, to develop innovative imaging biomarkers and multimodal deep learning models that integrate radiological, pathological, and clinical data to predict immunotherapy response and survival outcomes in gastric cancer patients. The project addresses a critical clinical need: while immune checkpoint inhibitors (ICIs) have transformed cancer treatment, only approximately 20% of gastric cancer patients achieve radiologic responses to ICI therapy, and current biomarkers such as programmed death-ligand 1 (PD-L1) expression, microsatellite instability, and tumor mutational burden have demonstrated limited predictive accuracy. The deliverables consist of three interconnected research components. Aim 1 develops a knowledge-guided multimodal deep learning model integrating contrast-enhanced computed tomography imaging, tumor microenvironment biomarkers from immunohistochemistry, and clinical factors to predict progression-free survival and immunotherapy response. Aim 2 builds a foundation model-informed deep learning framework utilizing hematoxylin and eosin whole-slide images and pathology reports, leveraging the research team's prior development of a large-scale histopathology foundation model trained on 130 million patches from over 104,000 slides across 25 organ types. Aim 3 combines all imaging modalities to create an integrated predictive platform for personalized gastric cancer treatment selection.Federal Project Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $526.9k | 7/10/26 |