Project Grant R44CA302212
- This National Cancer Institute (CFDA 93.394 Cancer Detection and Diagnosis Research) Project Grant award of $683,089 to the Sloan-Kettering Institute for Cancer Research supports the development of an improved, interpretable deep learning algorithm called DeepLIIF for more reproducible and accurate PD-L1 immunohistochemistry (IHC) biomarker quantification. The goal is to leverage virtual multiplex immunofluorescence (MPIF) restaining and large, diverse datasets across lung and bladder cancers to...
- This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) in the amount of $399,112 will be used by Imvaria Inc. to adapt its fully automated, AI-powered "DigitalBX" diagnostic tool for analyzing chest CT scans to detect pulmonary nodules suspicious of lung cancer. The goal is to provide an end-to-end automated diagnostic solution implemented as a secure cloud-based application that is fully reimbursable, alleviating the cost...
- This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $833,467 to the Dana-Farber Cancer Institute, Inc. (DFCI) to develop and deploy open-source AI tools to improve cancer clinical trial feasibility and recruitment. The key products and services to be delivered include: Extending DFCI's existing MatchMiner tool to match patients to clinical trials based on additional clinical variables beyond molecular criteria, such as...
- The National Cancer Institute (NCI) awarded a $700,611 Project Grant under the Cancer Cause and Prevention Research program (CFDA 93.393) to the Sloan-Kettering Institute for Cancer Research. The grant, titled "Practical Randomized Controlled Trial of Artificial Intelligence for Melanoma Diagnosis (PRACTA-MEL)," aims to determine the benefits and barriers to clinical adoption of AI systems for improving melanoma detection and reducing the number of unnecessary biopsies. The project...
- 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...
- This $194,022 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) supports research led by the Massachusetts General Hospital (MGH) to develop advanced deep learning models for predicting therapeutic response in cancer patients with brain metastases. The grant will fund the candidate, an oncologist at MGH, to build upon his expertise in...
- This Project Grant award from the National Cancer Institute (CFDA 93.398 Cancer Research Manpower) provides $147,486 to Case Western Reserve University to develop a "Pathologically Interpretable Computational Imaging Predictor for Response to Total Neoadjuvant Treatment in Rectal Cancers". The project aims to apply deep learning techniques to routinely acquired MRI scans to accurately identify rectal cancer patients who exhibit a complete clinical response to neoadjuvant therapy,...
- This $300,000 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), supports the development of interpretable machine learning tools to uncover imaging biomarkers associated with breast and lung cancer. The project aims to create dimension reduction techniques to visualize features from deep learning models, establish an interpretable...
- This $681,215 federal Project Grant was awarded on September 1, 2025 by the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to New York University (NYU) to develop AI-powered diagnostic tools for early prostate cancer detection. The project aims to leverage machine learning models to infer the presence of clinically significant prostate cancer using a minimal amount of degraded MRI data, in order to enable widespread, cost-effective population-level disease...
- This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $371,161 to develop advanced AI-based risk models for cervical cancer screening in low-resource settings. The project aims to leverage time-series imaging data and self-supervised learning to enhance the diagnostic accuracy of automated visual evaluation (AVE) models. The goal is to create a comprehensive risk stratification system that combines imaging data, HPV...
This $1,686,258 Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Onc.ai, Inc. aims to further develop and validate a deep learning radiomics biomarker for improved early response assessment in metastatic cancer therapy trials. The proposed approach uses deep learning models on CT scans at baseline and follow-up time points to generate a continuous "Serial CT Response Score" that can more accurately predict overall survival compared to the standard RECIST 1.1 criteria. This innovative fully automated method leverages signals both within and outside of tumor regions to supplement the limitations of RECIST in capturing early therapy response. The project period runs from August 2025 to July 2027.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $1.7m | 8/4/25 |