This $556,548 federal Project Grant award from the National Institute of Dental and Craniofacial Research (NIDCR) under the Oral Diseases and Disorders Research program (CFDA 93.121) will support the development of an artificial intelligence-driven system for online adaptive and personalized proton therapy (AID-ON-APPT). The key products to be delivered include: Two deep learning models for automated delineation of tumors and organs at risk on daily cone-beam CT images for head and neck cancer...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $546,874 Project Grant under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" (CFDA 93.286) program to Mayo Clinic to develop an AI-assisted quantitative photon-counting-detector CT imaging protocol for cytogenetic risk prediction and treatment response assessment in multiple myeloma patients. The project aims to leverage advanced imaging techniques and AI...
This 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), is funding research to improve the dose efficiency of photon counting computed tomography (CT) imaging. The total award funding is $373,383.00 and the project duration is from June 1, 2024 to March 31, 2028. The key objectives are to: 1) develop hardware components that can...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $1,495,790 Project Grant (Federal Grant ID R01EB034691) to The University of Texas Southwestern Medical Center for the "HIGH-PRECISION LUNG RADIOTHERAPY BY INTRA-TREATMENT DYNAMIC CONE-BEAM CT IMAGING AND DOSIMETRY-GUIDED PLAN ADAPTATION" project. The grant is funded under the NIBIB's "Discovery and Applied Research for Technological Innovations to Improve Human Health" program (CFDA 93.286)....
This $1,199,514 federal Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) aims to develop a comprehensive and scalable risk prediction model that fuses imaging and non-imaging data to enable early detection of pancreatic cancer in asymptomatic individuals. The project, titled "MULTIMODAL AI FUSION MODEL FOR EARLY DETECTION FOR PANCREATIC CANCER," will be conducted by the Mayo Clinic Arizona, a child entity of Mayo...
This Project Grant award from the National Cancer Institute (CFDA 93.395 - Cancer Treatment Research) provides $602,361 to Yale University to develop deep learning-based models for personalized dosimetry planning in prostate cancer radiopharmaceutical therapy (RPT) treatments. The project aims to leverage pre-treatment PET scans and during-treatment SPECT scans to build predictive models that can optimize the dose delivered to tumors and healthy tissues for each patient receiving 177Lu-PSMA RPT....
This Project Grant awarded by the National Cancer Institute (NCI), under the Federal Grant Program titled "Cancer Detection and Diagnosis Research" (CFDA 93.394), provides $1,637,339.00 to Yale University for a 5-year research project from May 1, 2024 to April 30, 2029. The project aims to develop robust deep learning-based approaches to accurately delineate target volumes, including gross tumor volumes (GTVs) and clinical target volumes (CTVs), for head and neck cancer patients...
This Project Grant award, totaling $613,827, was provided by the National Cancer Institute (NCI) under the Cancer Treatment Research federal grant program (CFDA 93.395). The grant supports research at Mayo Clinic Arizona, in collaboration with MD Anderson Cancer Center, Memorial Sloan Kettering, and the University of Georgia, to develop novel tools and methods for evaluating spot scanning proton therapy (SSPT) plans. Specifically, the project aims to create a Dose-LET Volume Histogram (DLVH)...
This $693,156 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 (CFDA 93.286) program will support the development of artificial intelligence (AI) algorithms to predict prognosis and aid in treatment selection for cutaneous squamous cell carcinoma (CSCC), a highly prevalent form of skin cancer. The research, led by the Dana-Farber Cancer Institute, Inc. in...
This $680,433 Project Grant award from the National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), aims to apply cutting-edge spatial proteogenomic technologies and pathology artificial intelligence (PAI) analysis to improve prognostic estimation and understand the underlying biology driving AI outcomes in prostate cancer. The key objectives are to: 1) examine the relationships between established genomic tests and PAI scores in predicting...