Project Grant R41CA306716
- 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 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 $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...
- The National Cancer Institute (NCI), under the federal Cancer Detection and Diagnosis Research Program (CFDA 93.394), awarded a $1,770,000 Project Grant to Raiz Diagnostics, Inc. to advance the clinical deployment and regulatory readiness of its LymphomaDx diagnostic assay. The grant aims to generate CLPA expression data from 2,500 clinically suspected lymphoma cases to train and validate a diagnostic classifier that can achieve 95% sensitivity/specificity for lymphoma detection and 90% top-1...
- The National Cancer Institute (NCI), part of the U.S. Department of Health and Human Services, awarded a $677,998 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to The Medical College of Wisconsin, Inc. (MCW). The project aims to externally validate and integrate radiopathomic models developed using autopsy and biopsy samples to better delineate the true extent of glioma tumors and identify microscopic infiltrating tumor cells. The research will refine...
- 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...
- The National Cancer Institute awarded a $717,667 Project Grant titled "Artificial Intelligence Enabled Stroma-Weighted Automated Grading System to Improve Risk Stratification in Black Men" under the Cancer Detection and Diagnosis Research program (CFDA 93.394). The grant aims to develop an AI-enabled automated grading system that leverages multiphoton microscopy and second harmonic generation imaging to analyze prostate cancer tumor biology and improve risk stratification, particularly...
- 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 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 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 ensure broad accessibility. The technology has already demonstrated promising results in two prospective clinical trials for head and neck cancer, reducing the risk of unnecessary radiation treatment to benign lymph nodes and improving patient quality of life. This Phase I STTR project will advance the essential components to facilitate widespread clinical adoption of this innovative lymph node malignancy identification system.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $306.8k | 8/6/25 |