Project Grant R21CA317725
- 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 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 New York University School of Medicine received a $681,215 Project Grant award from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) beginning September 1, 2025 and concluding August 31, 2030. The project aims to develop artificial intelligence (AI)-enabled magnetic resonance (MR) imaging technology to support point-of-care prostate cancer detection at the population level. The research challenges conventional...
- Federal Project Grant Award Summary New York University School of Medicine received a $700,171 Project Grant award dated July 16, 2026, from the National Cancer Institute under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to advance clinically translatable deep learning (DL) models for prostate cancer detection on magnetic resonance imaging (MRI). The project, with a completion date of June 30, 2031, addresses critical gaps in prostate MRI interpretation by developing robust...
- Federal Project Grant Award Summary The National Cancer Institute (NCI) awarded The Johns Hopkins University a Project Grant of $249,000 effective January 1, 2026 through December 31, 2028 under the Cancer Detection and Diagnosis Research program (CFDA 93.394). This award supports research to develop large-scale artificial intelligence (AI) and deep learning approaches to improve clinical outcomes in nuclear medicine imaging for cancer detection and diagnosis. The project addresses critical...
- Federal Grant Award Summary The University of Texas Southwestern Medical Center received a $426,828 Project Grant from the National Cancer Institute (NCI) under the Cancer Treatment Research program (CFDA 93.395) for the period June 1, 2026 through May 31, 2028. The award funds research directed at "Targeting Epigenetic Vulnerabilities in Osteosarcoma to Enhanced Immunotherapy," which aims to overcome two primary obstacles limiting immunotherapy efficacy in osteosarcoma—the most common...
- 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 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 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...
The National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), awarded $426,828 to The University of Texas Southwestern Medical Center on July 2, 2026, for an exploratory R21 project developing an artificial intelligence (AI)-based prognostic model for Ewing sarcoma using digital pathology images. The two-year project (completion date June 30, 2028) will leverage the largest Ewing sarcoma digital pathology dataset available globally—comprising over 900 cases from the Children's Oncology Group (COG)—to build and validate a computational model that predicts patient survival and risk stratification. Aim 1 will develop a prognostic model using whole-slide imaging (WSI) and pathology foundation models to identify reproducible image biomarkers of outcome; Aim 2 will independently validate the locked model using an independent cohort of approximately 100 cases from the Pediatric Cancer Data Commons without model retraining. This project addresses a critical clinical gap in pediatric oncology: current Ewing sarcoma risk stratification relies on broad clinical factors that fail to capture underlying histologic heterogeneity, leaving clinicians unable to tailor treatment intensity to individual patient risk. By combining quantitative AI-derived features from hematoxylin and eosin (H&E) whole-slide images with pathologist-defined histologic features, the research will establish the feasibility and analytic rigor of histology-based prognostic modeling in this rare pediatric cancer. All analyses will follow TRIPOD-AI (Transparent Reporting of Evaluations with Nonrandomized Designs—AI) reporting guidelines to ensure transparency and reproducibility, directly supporting the NCI's objective to develop accurate diagnostic techniques and methods for predicting disease course in cancer patients.Federal Project Grant Award Summary
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $426.8k | 7/7/26 |