Imaging, Biomarkers and Digital Pathomics for the Early Detection of Premetastatic Cancer and Precancerous Lesions Associated with Lethal Phenotypes (R01 Clinical Trial Optional) | PAR 22 131

Opportunity Information: Apply for PAR 22 131

This NIH grant opportunity (PAR 22-131) is an R01 funding announcement focused on pushing early cancer detection beyond traditional, single-test diagnostics by encouraging projects that fuse multiple kinds of biological and imaging evidence into one integrated decision-making framework. The central aim is to improve how clinicians and researchers detect (1) organ-confined cancers that are already biologically aggressive and likely to metastasize (premetastatic lethal phenotypes), and (2) precancerous lesions that later progress into life-threatening disease. In practical terms, the FOA is trying to help the field distinguish early which findings represent dangerous disease versus indolent or non-lethal conditions, so patients are neither missed (false negatives) nor unnecessarily alarmed and treated (false positives and overdiagnosis).

A defining feature of the announcement is its emphasis on multiparametric integration across scales. Applicants are encouraged to combine lower-resolution clinical or diagnostic data (for example, routine radiology acquisitions, standard pathology, or systemic biomarker tests from blood) with higher-resolution approaches that can resolve biology at the tissue, cellular, or even single-cell level. The FOA explicitly calls out integration of imaging with biomarkers and digital pathomics, and it also highlights the value of adding glycomics, metabolomics, and other "omics" layers, plus relevant metadata. The intention is not simply to collect many data types, but to connect them in a way that improves sensitivity and specificity for detecting aggressive disease early and for identifying high-risk precursor lesions before they evolve into lethal cancer.

Methodologically, the FOA is aligned with advanced computational and analytic strategies that can handle complex, high-dimensional data. It gives examples such as N-dimensional co-registered and cross-correlated imaging data integrated with multiplex biomarker readouts, digital pathology features, or glycomic/metabolomic imaging. It also explicitly welcomes modern analysis and visualization approaches, including artificial intelligence methods and even virtual reality-based visualization techniques, as long as they are used to meaningfully improve interpretation, integration, and diagnostic performance. Under the hood, this reflects a belief that the main bottleneck is often not a lack of measurements, but the uncertainty that arises when clinicians must reconcile conflicting signals across imaging, pathology, and molecular tests.

A major scientific theme running through the announcement is tumor heterogeneity and the need to characterize heterogeneous cell populations within tumors and precursor lesions. Many cancers contain subpopulations of cells with distinct molecular programs and behaviors, and small aggressive subclones can drive metastasis even when most of the lesion appears low risk. By supporting high-resolution analytics and imaging linked back to clinically available tests, the FOA is trying to enable tools that can spot those high-risk patterns earlier, more reliably, and in a way that is actionable in real-world clinical workflows.

This program is also structured around collaboration and standard-setting through participation in the Consortium for Imaging and Biomarkers (CIB) Research Program. Awardees are expected to be part of this broader consortium effort, which is designed to collectively improve diagnostic performance and decision-making. The CIB goals are clearly stated: develop better methods to identify potentially lethal cancers earlier (and separate them from non-lethal disease), reduce or better manage overdiagnosis, and reduce false positives and false negatives. In other words, the FOA is not only about innovation for innovation's sake; it is pointed at measurable improvements in diagnostic accuracy and clinical uncertainty.

From an administrative standpoint, this is a discretionary NIH grant using the R01 mechanism, with "Clinical Trial Optional" indicated, meaning applicants may propose clinical trials if appropriate, but they are not required to do so. The activity category listed is Education and Health, and the CFDA (assistance listing) number provided is 93.393. The opportunity is open to a wide range of applicant types, spanning government entities (state, county, city/township, special district), educational institutions (public and private higher education, independent school districts), tribal governments and organizations, public housing authorities/Indian housing authorities, nonprofits (both 501(c)(3) and non-501(c)(3)), for-profit organizations (other than small businesses), and small businesses. The FOA also explicitly highlights eligibility for a broad set of mission- and community-relevant organizations and institutions, including HBCUs, Hispanic-serving institutions, AANAPISISs, tribally controlled colleges and universities, Alaska Native and Native Hawaiian serving institutions, faith-based and community-based organizations, regional organizations, US territories/possessions, and even non-US (foreign) entities.

Key identifiers and timing details included in the source information are the funding opportunity title, the funding opportunity number (PAR 22-131), the sponsoring agency (National Institutes of Health), a creation date of 2022-04-12, and an original closing date listed as 2025-09-07. The award ceiling and expected number of awards are not specified in the provided text, which usually signals that budgets and award counts may depend on annual appropriations, program priorities, and the merit of submitted applications.

Overall, this FOA is aimed at teams that can bridge radiology, pathology, molecular profiling, and advanced computation to produce integrated platforms that reduce diagnostic ambiguity. The work NIH is trying to stimulate here is the kind that makes early detection not just earlier, but smarter: detecting the cases that will become lethal while avoiding unnecessary intervention for findings that are unlikely to harm patients.

• The National Institutes of Health in the education, health sector is offering a public funding opportunity titled "Imaging, Biomarkers and Digital Pathomics for the Early Detection of Premetastatic Cancer and Precancerous Lesions Associated with Lethal Phenotypes (R01 Clinical Trial Optional)" and is now available to receive applicants.
• Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 93.393.
• This funding opportunity was created on 2022-04-12.
• Applicants must submit their applications by 2025-09-07. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
• Eligible applicants include: State governments, County governments, City or township governments, Special district governments, Independent school districts, Public and State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities/Indian housing authorities, Native American tribal organizations (other than Federally recognized tribal governments), Nonprofits having a 501 (c) (3) status with the IRS, other than institutions of higher education, Nonprofits that do not have a 501 (c) (3) status with the IRS, other than institutions of higher education, Private institutions of higher education, For-profit organizations other than small businesses, Small businesses, Others.

Apply for PAR 22 131

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