Project Grant K08EB037077

Award Date 8/1/25
Completion Date 7/31/29
Dollars Obligated $194K
Federal Grant Program
93.286
Assistance Type
Project Grant
Place of Performance
Massachusetts, USA
Similar Awards
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 Project Grant award from the National Cancer Institute (CFDA 93.395 Cancer Treatment Research) provides $2,104,477 to The General Hospital Corporation (Massachusetts General Hospital) to conduct research aimed at elucidating resistance mechanisms and enhancing response to immune checkpoint blockade in central nervous system (CNS) metastases from breast cancer. The research involves profiling cancer genetic and immune phenotypic changes in samples collected from patients before, during,...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) has awarded a $693,156 Project Grant (CFDA 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health) to the Dana-Farber Cancer Institute, Inc. (DFCI) to develop artificial intelligence (AI) algorithms for predicting prognosis and optimizing treatment selection for cutaneous squamous cell carcinoma (CSCC), a highly prevalent form of skin cancer. The project aims to train and validate AI...
This $676,006 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) will fund research to develop non-invasive imaging strategies to identify mechanisms of resistance to cancer immunotherapy early after treatment. The researchers will utilize PET imaging to measure markers of immune response and neutrophil levels, as well as implantable fluorescence sensors to continuously monitor these parameters in vivo. They will pair these molecular...
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 system for cytogenetic risk prediction and treatment response assessment in multiple myeloma patients. The project aims to: Develop and validate a deep learning imaging model...
This National Cancer Institute (NCI) Project Grant under CFDA 93.394 "Cancer Detection and Diagnosis Research" program will fund the development of a virtual multiplex immunofluorescence (MPIF) restaining algorithm, DeepLIIF, to improve the reproducibility and accuracy of PD-L1 immunohistochemistry (IHC) biomarker quantification. The $683,089 award to the Sloan-Kettering Institute for Cancer Research aims to incorporate MPIF immune cell markers, whole-cell segmentation, and large...
This Project Grant award of $703,540 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) supports the development and optimization of implantable microdevices (IMDs) to predict optimal cancer therapy for individual patients. The primary awardee, Brigham & Women's Hospital, will conduct a clinical study in head and neck cancer patients to validate the IMD's ability to effectively predict response to immunotherapy and chemotherapy. The project also aims...
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), aims to develop rapid, motion-robust, and low-gadolinium magnetic resonance imaging (MRI) technologies for pediatric brain tumor imaging. The $1,336,000 project, awarded to The General Hospital Corporation (doing business as Massachusetts General Hospital), seeks to address...
The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $412,801 Project Grant (CFDA 93.286: Discovery and Applied Research for Technological Innovations to Improve Human Health) to Brigham & Women's Hospital Inc. to develop an abbreviated 8-minute brain MRI protocol that captures a comprehensive set of quantitative and qualitative MRI contrasts, including time-resolved gadolinium-based enhancement. The goal is to maximize the value of MRI exams by reducing scan...
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...

The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $194,022 Project Grant under the Discovery and Applied Research for Technological Innovations to Improve Human Health (CFDA 93.286) program to The General Hospital Corporation (doing business as Massachusetts General Hospital) to develop deep learning models that can integrate radiology, histopathology, and clinico-genomic data to predict response to immune checkpoint inhibitor (ICI) therapy for brain metastases.

The key objectives are to: 1) develop a deep learning model using pre-treatment brain MRI to predict ICI efficacy, 2) develop a separate model using histopathology (H&E) data to predict ICI efficacy, and 3) create a multi-modal fusion model integrating MRI, H&E, and clinico-genomic data to predict ICI response. The performance of these models will be compared to current clinical biomarkers like PD-L1 expression. The project aims to lay the groundwork for a future R01-funded effort to validate the multi-modal fusion strategy for predicting ICI response in brain metastases patients.

Generated 7/1/25, 5:24 AM