This Project Grant award of $572,490.00 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) will fund the development of PILLAR, an AI-based tool to predict breast cancer risk from longitudinal multi-modal breast imaging data. The project aims to create novel machine learning architectures and self-supervised learning algorithms to improve the accuracy of cancer risk assessment over current clinical models. Additionally, the project will develop methods...
This $700,611 Project Grant, awarded on March 11, 2025 by the National Cancer Institute (NCI) under the Cancer Cause and Prevention Research (CFDA 93.393) program, supports a practical randomized controlled trial (RCT) of artificial intelligence (AI) for melanoma diagnosis at Memorial Sloan Kettering Cancer Center (MSK) and Stanford University (SU). The goal is to determine the potential benefits and barriers to clinical adoption of AI-assisted dermoscopy, and to evaluate the impact on the...
This federal Project Grant award of $505,613, provided by the National Cancer Institute (CFDA 93.394 Cancer Detection and Diagnosis Research), will support the development of statistical and computational approaches to harness genomic data and translate findings into precision prevention strategies for cancer. The key objectives are to: Develop deep learning-based methods to identify tumor subtypes linked to adverse outcomes and robust data integration approaches to assess associations between...
The National Cancer Institute (NCI) awarded a $570,634 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the University of Connecticut to develop a robust, multimodal, and longitudinal artificial intelligence (AI) system to enhance breast cancer screening. The overarching objective is to optimize and personalize breast cancer screening by advancing AI algorithms that can transform clinical decision-making. The key products/services to be delivered...
This federal Project Grant award, provided by the National Cancer Institute (NCI) under the Cancer Cause and Prevention Research program (CFDA 93.393), supports a $644,804 research project titled "Advancing Breast Cancer Risk Assessment for Black Women." The project aims to develop and validate a novel deep learning-based breast cancer risk assessment tool focused on Black women, leveraging digital breast tomosynthesis (DBT) imaging data. Key objectives include: Developing a DBT-driven...
This federal Project Grant award from the National Cancer Institute (NCI) under the Cancer Cause and Prevention Research program (CFDA 93.393) provides $1,043,109 to Isono Health, Inc. to develop and validate artificial intelligence (AI) models for its ATUSA 3D breast ultrasound imaging platform. The goal is to create lesion classification, lesion segmentation, and breast density calculator AI models to improve the ATUSA system's ability to accurately identify and diagnose breast cancer at an...
This federal Project Grant award of $680,433 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to The Regents of the University of California, San Francisco (UCSF) aims to apply advanced spatial proteogenomic and artificial intelligence technologies to improve prognostic estimation and understand the underlying biology driving pathology AI algorithms for prostate cancer. The key objectives are to: 1) explore the relationships between standard-of-care...
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 93.393 - Cancer Cause and Prevention Research) to Emory University for $140,511 supports the development of a computational framework that leverages AI visual explanation to guide AI-based abdominal cancer diagnostic imaging. The key objectives are to: 1) Improve AI sample efficiency through visual explanation supervision of cancer imaging annotations, 2) Consolidate AI's knowledge across multi-institutional data while...
The National Cancer Institute (NCI) awarded a $1,199,514 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the Mayo Clinic Arizona to develop a comprehensive, fair, and scalable multimodal AI model called PRECISE that combines imaging and non-imaging data to enable early detection of pancreatic cancer. Key aims include: 1) developing deep learning models to segment imaging biomarkers from abdominal CT scans, 2) creating a fusion model using a graph neural...