Project Grant R03EB027900

This Project Grant award from the National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), will fund the development of a deep learning algorithm called DeepLIIF to improve reproducible and accurate PD-L1 immunohistochemistry (IHC) biomarker quantification. The $683,089 award, effective December 1, 2024 through November 30, 2029, will enable the Sloan-Kettering Institute for Cancer Research to incorporate multiplex immunofluorescence (MPIF) immune...

This Project Grant award from the National Cancer Institute (NCI) under the Cancer Detection and Diagnosis Research program (CFDA 93.394) provides $593,383 to develop an AI-augmented, multimodal, label-free nonlinear optical microscopy system for rapid and precise diagnosis of thyroid cancer and lymph node metastasis. The proposed system integrates coherent anti-Stokes Raman scattering microscopy, second harmonic generation microscopy, and two-photon autofluorescence microscopy, aiming to...

This Project Grant award from the National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), provides $245,087 in funding to the University of Delaware to develop advanced AI-assisted prostate MRI interpretation capabilities. The key objectives of this 2-year project are to: 1) Curate a comprehensive dataset by annotating public and University of Delaware Memorial Sloan Kettering (MSK) MRI scans with corresponding radiologist-generated PIRADS...

This federal Project Grant award of $572,490.00 from the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) supports 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 compared to current models. Additionally, the team will develop methods...

The National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), awarded a $164,639 Project Grant to Brown University to develop a novel deep learning-based pipeline for segmentation and registration of kidney tumors for image-guided thermal ablation (IGTA) procedures. The research aims to improve the accuracy of IGTA treatment by leveraging advanced image analysis techniques to better detect and mitigate undetected incomplete treatment, ultimately...

The Regents of the University of Colorado, doing business as the University of Colorado-Denver, received a $388,467 Project Grant award from the National Cancer Institute under the federal Cancer Detection and Diagnosis Research program (CFDA 93.394) to develop an automated, generalizable lesion detection system called GELES for positron emission tomography-computed tomography (PET/CT) imaging of gastroenteropancreatic neuroendocrine tumors. The two-year project aims to create deep...

The National Cancer Institute (NCI), under the federal Cancer Detection and Diagnosis Research program (CFDA 93.394), awarded a $717,667 Project Grant to the Icahn School of Medicine at Mount Sinai. The grant aims to develop an Artificial Intelligence (AI)-enabled, stroma-weighted automated grading system (SWAG) to improve risk stratification and early detection of lethal prostate cancer phenotypes in Black men. Key objectives include: 1) Annotating H&E and multiphoton microscopy (MPM)...

The National Cancer Institute (NCI) awarded a $1,199,514 Project Grant under the Cancer Detection and Diagnosis Research program (CFDA 93.394) to Mayo Clinic Arizona to develop a "MULTIMODAL AI FUSION MODEL FOR EARLY DETECTION FOR PANCREATIC CANCER." The project aims to create a comprehensive risk prediction model, termed "PRECISE," that combines imaging biomarkers from computed tomography (CT) scans and clinical data from electronic medical records to enable early...

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 federal Project Grant award of $696,133 from the National Cancer Institute (NCI), under the Cancer Detection and Diagnosis Research program (CFDA 93.394), aims to develop a multimodal imaging platform to track the spatial and temporal migration of neural stem cells (NSCs) to brain metastases of breast cancer. The award is supporting research at Northwestern University to optimize an integrated single-photon emission microscope (SPEM) and magnetic resonance (MR) system for high-resolution,...