Project Grant R01CA297227
- This $593,383 Project Grant, awarded by the National Cancer Institute under the Cancer Detection and Diagnosis Research program (CFDA 93.394), is supporting the development of an AI-augmented, multimodal, label-free nonlinear optical microscopy system for rapid and precise diagnosis of thyroid cancer and lymph node metastasis. The primary awardee, The Methodist Hospital Research Institute, is collaborating with The Johns Hopkins University on this project. The proposed system aims to eliminate...
- This Project Grant award of $306,798 from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to develop an artificial intelligence-based system to accurately identify malignant lymph nodes. The key objectives are to: Create a lymph node segmentation model to enable extraction of radiomic features critical for malignancy classification. Develop both cloud-based and standalone desktop deployment options for the classification model to...
- This federal 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 improve the diagnostic accuracy and cost-effectiveness of breast cancer screening through the development of an AI system. The $672,087 grant, awarded on August 12, 2025, will fund a research project at New York University School of Medicine to build a...
- 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 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 $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 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 Project Grant award for $402,728, provided by the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394), aims to develop innovative deep learning models for multi-modal imaging analysis to improve the accuracy and interpretability of gastric cancer prognosis. The primary goal is to leverage Computed Tomography (CT) and whole-slide pathological images (WSI) to create interpretable deep learning models that can provide more accurate and...
- This federal Project Grant award of $714,110.00 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) will support research by New York University School of Medicine to translate insights in follicular lymphoma for improved diagnosis and classification. The research aims to identify reliable predictive biomarkers to guide frontline risk stratification and treatment for high-risk follicular lymphoma patients, who face a poor prognosis due to early disease...
- 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 $2,867,231 federal Project Grant award from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Duke University will fund research to develop a novel multi-task deep learning model for predicting high-risk versus low-risk thyroid nodules using ultrasound imaging. The goal is to improve the analysis of thyroid nodules and reduce overdiagnosis and unnecessary biopsies for thyroid cancer, which currently accounts for up to 77% of thyroid cancer diagnoses. The research will establish a comprehensive repository of thyroid nodule data to better understand the incidence of high-risk nodules and rates of unnecessary biopsies. This innovative approach aims to foundationally change how thyroid nodules are analyzed and managed, reducing unnecessary patient exposure and healthcare costs associated with overdiagnosis.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $2.9m | 8/27/25 |