Project Grant R44NS120796
- This $194,022 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 research led by the Massachusetts General Hospital (MGH) to develop advanced deep learning models for predicting therapeutic response in cancer patients with brain metastases. The grant will fund the candidate, an oncologist at MGH, to build upon his expertise in...
- This $997,410 Project Grant award from the National Cancer Institute under the Cancer Detection and Diagnosis Research program (CFDA 93.394) aims to develop non-invasive MRI-based techniques to detect and differentiate between the invasive neuronal (NEU) and glycolytic/plurimetabolic (GPM) subtypes of high-grade glioma brain tumors. The project leverages recent discoveries about these tumor subtypes' distinct microstructural, metabolic, and synapse profiles to enable more comprehensive tumor...
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB) has awarded a $693,156 Project Grant (CFDA 93.286 Discovery and Applied Research for Technological Innovations to Improve Human Health) to the Dana-Farber Cancer Institute, Inc. (DFCI) to develop artificial intelligence (AI) algorithms for predicting prognosis and optimizing treatment selection for cutaneous squamous cell carcinoma (CSCC), a highly prevalent form of skin cancer. The project aims to train and validate AI...
- This 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 develop advanced deep learning algorithms to enhance the management of multiple myeloma (MM), a type of hematologic cancer. The $755,336 award, with a period of performance from August 1, 2025 to June 30, 2029, will focus on two key objectives: 1) Developing automated...
- 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...
- This Project Grant award of $742,261.00 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 advance head computed tomography (CT) interpretation through three interconnected approaches focused on knowledge representation, image-based report verification, and longitudinal analysis of sequential scans. The project, led by President and Fellows of...
- This $399,358 Project Grant awarded by the National Cancer Institute (CFDA 93.394 - Cancer Detection and Diagnosis Research) to Cordance Medical Inc. aims to optimize a non-invasive focused ultrasound system, called the "NeuroAccess System," to enable liquid biopsies for brain tumor patients. The key objectives are to demonstrate the safety of repeatedly opening the blood-brain barrier, optimize the opening volume and technique in a rat glioma model, and confirm the system's ability to...
- This SBIR Phase II Cooperative Agreement awarded by the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) provides $1,200,991 in funding to Mindtrace Technologies, Inc. to complete the development of a novel software platform that integrates multiple data sources to support quantitative pre-operative surgical planning and evidence-based prediction of post-operative cognitive outcomes for neurosurgery patients. The project aims to create a...
- This Project Grant award from the National Science Foundation (CFDA 47.084 - NSF Technology, Innovation, and Partnerships) provides $249,979 to the H. Lee Moffitt Cancer Center and Research Institute Hospital, Inc. to develop a Trustworthy Artificial Intelligence (AI) solution for identifying and marking the boundaries of brain tumors in magnetic resonance imaging (MRI) scans. The project, titled "PFI-TT: TRUSTWORTHY ARTIFICIAL INTELLIGENCE FOR THE VOLUMETRIC EVALUATION OF BRAIN...
- 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...
TOWARDS A VIRTUAL BIOPSY: AN IMPROVED MULTIMODAL IMAGING BIOMARKER TO GUIDE TREATMENT DECISIONS IN NEURO-ONCOLOGY BY COMBINING ADVANCED TISSUE MICROSTRUCTURE IMAGING WITH DEEP LEARNING - PROJECT SUMMARY/ABSTRACT DESPITE ADVANCES IN SURGERY, RADIOTHERAPY, AND CHEMOTHERAPY, THE PROGNOSIS FOR NEURO- ONCOLOGY PATIENTS REMAINS POOR, WITH A MEAN SURVIVAL OF 12-15 MONTHS FOR HIGH-GRADE GLIOMAS. ONE MAJOR REASON FOR POOR SURVIVAL IS THAT IT REMAINS DIFFICULT TO ACCURATELY ASSESS TUMOR PROGRESSION AND TREATMENT-RELATED CHANGES ON STANDARD IMAGING. THIS LIMITS NECESSARY INFORMATION FOR GUIDING BIOPSIES OR RESECTING MALIGNANT TISSUE. IN ADDITION, NEW THERAPIES CAN CAUSE RADIOLOGICAL PATTERNS THAT OBFUSCATE THE UNDERLYING COURSE OF THE DISEASE. AS A RESULT, THERE IS A CRITICAL NEED FOR NEW QUANTITATIVE IMAGING TOOLS TO EVALUATE BRAIN TUMORS AND THEIR PROGRESSION. THE GOAL OF THIS PROPOSAL IS TO DEVELOP NOVEL QUANTITATIVE IMAGING BIOMARKERS USING THE COMBINATION OF ADVANCED TISSUE MICROSTRUCTURE IMAGING AND DEEP LEARNING TO ACCURATELY DISCRIMINATE TUMOR FROM NON-TUMOR TISSUE, MEASURE TUMOR PROGRESSION AND TREATMENT RESPONSE, AND PREDICT CLINICAL OUTCOMES. THIS APPROACH UTILIZES RESTRICTED SPECTRUM IMAGING (RSI), AN ADVANCED DIFFUSION-WEIGHTED IMAGING TECHNIQUE THAT MODELS THE RESTRICTED DIFFUSION OF WATER TO IMPROVE TUMOR CONSPICUITY. PHASE I OF THIS PROPOSAL WILL DEVELOP A HYBRID MULTIMODAL, RSI-BASED BIOMARKER FOR BRAIN CANCER, QUANTIFY THE BIOMARKER IN ABNORMAL SUB-REGIONS, AND DEMONSTRATE THE BIOMARKER'S PERFORMANCE IN PREDICTING CLINICAL OUTCOMES. PHASE II OF THIS PROPOSAL WILL DEVELOP AND DEPLOY COMMERCIAL-GRADE SOFTWARE TO THE CORTECHS LABS CLOUD PLATFORM, DEMONSTRATE ITS CLINICAL USABILITY AND UTILITY, AND GENERATE THE MATERIALS REQUIRED FOR A 510K FDA SUBMISSION. THE AI TECHNOLOGY DEVELOPED THROUGH THIS PROPOSAL WILL ULTIMATELY SERVE AS A CLINICAL DECISION SUPPORT TOOL TO IMPROVE CLINICIAN PERFORMANCE IN DIAGNOSING AND EVALUATING BRAIN TUMORS AND PREDICTING TUMOR RESPONSE TO TREATMENT.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $0 | 7/8/24 | ||
| Not listed | $0 | 3/31/23 | ||
| Not listed | $254.7k | 9/22/21 | ||
| Not listed | $254.7k | 9/22/21 |