Imaging, Biomarkers and Digital Pathomics for the Early Detection of Premetastatic Cancer and Precancerous Lesions Associated with Lethal Phenotypes (R01 Clinical Trial Optional)

Organization
NCI
Type
NIH
Number
PAR-22-131
Brief Description

This Funding Opportunity Announcement (FOA) will support the development of state-of-the-art projects that integrate imaging, biomarkers, digital pathomics, glycomics, metabolomics, other omic information and/or meta data obtained from platforms including but not limited to lower resolution diagnostic acquisitions and systemic biomarker results to high resolution single-cell analytics / imaging applied to the characterization of heterogeneous cell populations within tumor for improving current approaches for: (1) the early detection of organ confined premetastatic aggressive cancer, and, (2) identifying precancerous lesions associated with the development of a subsequent lethal phenotype. This FOA specifically attempts to address and improve diagnostic uncertainty in clinical decisions by improving detection sensitivity and specificity of integrated multiparametric platforms. For example, N-dimensional co-registered, cross-correlated imaging data integrated with multiplexed biomarker results and/or digital pathomics, glycomics, or metabolomic imaging using analytic strategies such as artificial intelligence or virtual reality visualization techniques. The projects supported by this FOA will collectively participate in the existing Consortium for Imaging and Biomarkers (CIB) Research Program. The goals of the CIB are to: (1) improve diagnostic performance by developing methodology for the early identification of potentially lethal cancer versus non-lethal disease, (2) minimize/better manage overdiagnosis and (3) reduce false positives and false negatives.

This FOA will utilize the NIH Research Project Grant (R01) mechanism and is suitable for projects where proof-of-principle of the individual proposed methodologies have already been established and supportive preliminary data are available.