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...
This federal Project Grant award from the National Cancer Institute (CFDA 93.393 - Cancer Cause and Prevention Research) provides $653,500.00 to Emory University to develop improved methods for measuring the completeness of cancer registries in the United States. The key products and services to be delivered under this 5-year grant include: Developing advanced statistical methods to estimate cancer registry completeness that account for factors such as cancer type, demographics, geography, and...
The National Cancer Institute (NCI) awarded a $564,414 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the Cleveland Clinic Lerner College of Medicine of Case Western Reserve University. The grant will fund a 5-year research project to: 1) Develop and validate a predictive model based on electronic health record data that can accurately identify individuals at high risk for gastric cancer, and 2) Develop a mathematical model to assess the potential...
The National Cancer Institute (NCI) has awarded a $554,653 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to the Institute for Systems Biology (ISB) in Seattle, WA. The 4-year grant titled "RARECYTEFINDER: A BENCH-TO-BITS TOOLKIT FOR LABEL-FREE, WHOLE-SPECTRUM ANALYSIS OF RARE DISSEMINATED TUMOR CELLS IN LIQUID AND TISSUE BIOPSIES" aims to develop an innovative, unbiased method for identifying and analyzing rare disseminated tumor cells (DTCs)...
This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Yale University totals $167,500 and will be executed from September 2023 through August 2025. The primary objectives are to: Develop deep neural network-based emulation analysis methods and software to objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on cancer prognosis, especially when randomized clinical trials are not feasible. The...
The National Cancer Institute (NCI) awarded a $386,700 Project Grant under CFDA 93.394 "Cancer Detection and Diagnosis Research" to the Regents of the University of Minnesota. The grant, titled "Preclinical Development and Validation of Targeted Mass Spectrometry Assays for Serous Ovarian Cancer Diagnosis", will fund research to develop and validate novel multiplexed protein biomarker assays that can differentiate serous ovarian cancer patients from healthy individuals. The...
This federal Project Grant award of $402,728 from the National Cancer Institute's Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to develop innovative interpretable deep learning models for multi-modality imaging in cancer prognostic assessment, with a focus on improving the accuracy and interpretability of prognosis predictions for gastric cancer patients. The funded research project at Wake Forest University Health Sciences will integrate domain knowledge from physician...
This $388,673 Project Grant awarded by the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) supports the development of novel statistical methods to identify and evaluate predictive biomarkers for targeted cancer therapies using data from non-randomized Phase II clinical trials. The key products of this 2-year project, led by the University of Kentucky Research Foundation, include: Novel semiparametric statistical models to identify and...
This $989,122 Project Grant award from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to evaluate the performance of four commercial mammography-based artificial intelligence (AI) algorithms for breast cancer risk prediction in diverse U.S. screening populations. The project will assess the accuracy and equity of these AI risk models compared to traditional clinical risk factor-based models, using data from the Breast Cancer...
This Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) provides $627,474 to the Dana-Farber Cancer Institute to conduct research aimed at leveraging innovative epigenomic methods and signatures to inform the understanding of prostate cancer lineage plasticity, including treatment-emergent neuroendocrine prostate cancer (NEPC). The key goals are to utilize epigenomic signatures to detect NEPC and predict enzalutamide resistance, as well...