Project Grant R01CA299626
- Federal Grant Award Summary The National Cancer Institute awarded the University of Chicago a $249,000 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) for the period of April 1, 2026 through March 31, 2029. This research initiative develops explainable artificial intelligence (AI) models to predict early-stage melanoma recurrence by integrating multiplexed tissue imaging, spatial transcriptomics, and histopathological data. The project addresses a critical...
- Federal Grant Award Summary The University of California, San Francisco (UCSF) received a $729,587 Project Grant from the National Cancer Institute under the Cancer Treatment Research program (CFDA 93.395) awarded August 21, 2025, with completion targeted for July 31, 2030. The project aims to develop a personalized, voxel-wise predictive model for identifying brain metastases risk in non-small cell lung cancer adenocarcinoma patients using multi-parametric magnetic resonance imaging. The...
- Federal Project Grant Award Summary The University of California, Los Angeles received a $640,956 Project Grant from the National Cancer Institute under the Cancer Detection and Diagnosis Research Program (CFDA 93.394) awarded on May 15, 2026, with a completion date of April 30, 2031. This grant supports the development of an artificial intelligence-enhanced perfusion magnetic resonance imaging (MRI) tool designed to optimize yttrium-90 (Y90) microsphere distribution in radioembolization...
- Federal Project Grant Award Summary The National Cancer Institute awarded a $382,449 Project Grant to the University of California, Los Angeles under the Cancer Detection and Diagnosis Research program (CFDA 93.394) on July 16, 2025, with completion targeted for June 30, 2027. This research project develops and validates advanced dual-nuclei magnetic resonance imaging (MRI) techniques to non-invasively differentiate between recurrent brain metastases (RBM) and radiation-induced necrosis (RN)...
- The National Cancer Institute (NCI) has awarded a $572,490 Project Grant to The Regents of the University of California, San Francisco (UCSF) under the federal Cancer Detection and Diagnosis Research program (CFDA 93.394). The project, titled "PILLAR: Multi-Modal Imaging AI Models for Breast Cancer Risk", aims to develop an AI-powered tool to predict breast cancer risk using longitudinal data from mammograms, tomosynthesis, and MRI scans. The goal is to create more accurate cancer risk...
- 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 The National Cancer Institute (NCI) awarded a $414,402 Project Grant to The Regents of the University of California, San Francisco under the Cancer Treatment Research program (CFDA 93.395) for the period May 1, 2026 through April 30, 2028. This research project develops multi-modality imaging and biomarker integration methods to improve glioblastoma radiation therapy targeting. The deliverables include: (1) white matter infiltrative risk maps derived from diffusion...
- Federal Grant Award Summary The National Cancer Institute awarded a $371,161 Project Grant to the University of Colorado-Denver under the Cancer Detection and Diagnosis Research Program (CFDA 93.394) on September 1, 2025, for a two-year performance period concluding August 31, 2027. The award funds the development of artificial intelligence (AI) algorithms designed to improve cervical cancer screening in low-resource settings. Specifically, the project will create advanced AI risk stratification...
- Federal Project Grant Award Summary The National Cancer Institute (NCI) awarded $591,468 on August 6, 2025, under the Cancer Detection and Diagnosis Research Program (CFDA 93.394) to The Research Foundation For The State University of New York, doing business as SUNY Oswego. This three-year project, extending through July 31, 2028, will develop explainable analogical learning models for cancer diagnosis and prediction based on deep pathophysiology. The research will integrate heterogeneous...
- Federal Project Grant Award Summary The National Cancer Institute (NCI) awarded The Leland Stanford Junior University a Project Grant of $119,172.00 under the Cancer Research Manpower program (CFDA 93.398) to support research addressing therapeutic response in non-small cell lung cancer (NSCLC). The project, initiated August 1, 2025, and completing July 31, 2027, focuses on developing computational approaches to integrate multi-modal and multi-omics biomedical datasets for improved...
The National Cancer Institute, under the Cancer Cause and Prevention Research program (CFDA 93.393), awarded the University of California, Los Angeles $825,627 beginning May 1, 2026, to develop advanced machine learning models for improving breast cancer prognosis prediction. The project, titled "Vision-Language Models with Explainability for Breast Cancer Prognosis Using Image and Clinical Data," will deliver three primary research products: (1) benchmark performance assessments of existing prognostic models using clinical data and digital pathology images; (2) a novel "Survivor Model" that integrates whole slide imaging (WSI) data with traditional clinical prognostic factors to predict patient recurrence and survival outcomes at 5, 10, and 15-year intervals; and (3) an explainer model designed to generate pathologist-interpretable text descriptions of predictive image features, enhancing transparency and clinical utility of the vision-language technology. The five-year project (completion date April 30, 2031) addresses limitations in current clinical risk assessment tools such as the Nottingham Index by leveraging digital pathology to capture tumor characteristics beyond human visual discrimination, including nuclear heterogeneity and chromatin complexity. The research team will expand their QuiltNet vision-language model platform to merge WSI analysis with clinical data, offering more comprehensive prognostic tools for precision medicine and improved patient outcomes in breast cancer treatment planning and risk stratification.Federal Project Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $825.6k | 4/22/26 |