Imaging, Biomarkers and Digital Pathomics for the Early Detection of Premetastatic Aggressive Cancer (R01 Clinical Trial Optional) | PAR 19 264

Frequently Asked Questions (FAQs)

What is the name of this NIH grant opportunity?

The opportunity is titled "Imaging, Biomarkers and Digital Pathomics for the Early Detection of Premetastatic Aggressive Cancer (R01 Clinical Trial Optional)" and is identified as PAR 19-264.

What is the main purpose of PAR 19-264?

The main purpose is to push early cancer detection beyond current limitations by developing methods that can detect premetastatic aggressive cancer earlier and, critically, distinguish truly dangerous (lethal/aggressive) disease from indolent (slow-growing, less clinically meaningful) findings. A core aim is improving patient benefit by reducing unnecessary follow-up workups and treatments that can result from overdiagnosis or false alarms.

What does "premetastatic aggressive cancer" mean in this FOA?

In this FOA, premetastatic aggressive cancer refers to cancers (or precancerous lesions) that have not yet spread (metastasized) but are biologically poised to behave aggressively. The emphasis is on identifying disease that is likely to progress and cause harm if not treated appropriately.

Why does the FOA emphasize distinguishing lethal disease from indolent disease?

The FOA is built around the idea that earlier detection only helps patients when it can reliably separate disease that will become clinically serious from abnormalities that are unlikely to progress. Without that distinction, screening and early detection can increase overdiagnosis, overtreatment, patient anxiety, and avoidable procedures.

What kinds of approaches does this FOA support?

The FOA supports state-of-the-art projects that develop and rigorously characterize integrated approaches combining imaging, biomarkers, and digital pathomics. The program strongly emphasizes data integration rather than single-modality advances.

Is this FOA focused on a single technology or a specific imaging modality?

No. The FOA is intentionally technology agnostic. It does not require a specific imaging modality, biomarker class, or computational approach. Instead, it prioritizes measurable improvements in diagnostic performance and clinical relevance.

What does "integrated" or "multi-modal" mean in the context of this FOA?

Here, integrated or multi-modal means combining multiple data streams such as imaging data, biomarker results, and computational pathology outputs, and then analyzing them together in a coordinated way. The FOA highlights the value of aligning these data in space and time and fusing features across modalities to improve sensitivity and specificity.

What is "digital pathomics" in this opportunity?

Digital pathomics refers to computational pathology derived from digitized tissue slides, where quantitative features are extracted and analyzed to support detection and risk stratification. In this FOA, digital pathomics is intended to be part of an integrated system alongside imaging and biomarker data.

Are "omics" data allowed under this FOA?

Yes. The FOA allows incorporation of other "omics" data streams (for example, genomic, transcriptomic, proteomic, metabolomic, or epigenomic signals) when they strengthen performance. The emphasis remains on integration and clinically meaningful gains rather than adding data types without clear benefit.

What does the FOA mean by "N-dimensional, co-registered and cross-correlated imaging data"?

This refers to using multiple imaging types and features and aligning them (co-registering) across space and time, then analyzing their relationships (cross-correlation). The intent is to capture complementary signals that a single imaging modality may miss.

What outcomes or improvements is the FOA trying to achieve?

The FOA aims to produce detection and risk-stratification methods that are both more sensitive (finding real aggressive disease earlier) and more specific (avoiding alarms for findings that will not become clinically meaningful). It also aims to reduce diagnostic uncertainty in clinical decision-making.

How does the FOA address overdiagnosis?

Overdiagnosis is explicitly highlighted as a major issue in screening programs. The FOA prioritizes approaches that minimize or better manage overdiagnosis by improving the ability to identify lethal cancer early while avoiding the downstream harms of detecting and treating non-lethal disease.

How does the FOA address false positives and false negatives?

A central program-level goal is to reduce false positives and false negatives. False positives can lead to anxiety and unnecessary invasive follow-up procedures, while false negatives can lead to missed opportunities for early treatment. The FOA emphasizes improving diagnostic performance in ways that matter clinically and in real-world care settings.

Are artificial intelligence (AI) methods encouraged?

Yes. The FOA explicitly encourages AI and other advanced computational methods for pattern recognition and feature fusion across modalities as part of integrated analytic strategies.

Does the FOA mention any specialized visualization approaches?

Yes. Visualization approaches, including virtual reality visualization, are highlighted as potential tools to explore complex, multi-dimensional datasets and uncover relationships that may be difficult to see with conventional displays.

What is the relationship between this FOA and the Consortium for Imaging and Biomarkers (CIB) Research Program?

Projects funded under this FOA are expected to align with and contribute to the broader CIB Research Program. This means awardees are not expected to operate as isolated labs; the work should support the consortium's clinically grounded goals around diagnostic performance and reduction of harms.

What are the CIB program-level goals referenced in the FOA?

The goals described are: (1) improve diagnostic performance by developing methods that can identify lethal cancer early and distinguish it from non-lethal disease, (2) minimize or better manage overdiagnosis, and (3) reduce false positives and false negatives that drive anxiety, unnecessary follow-up, missed early treatment opportunities, and inefficiency in care.

What is the funding mechanism for this opportunity?

The funding mechanism is an NIH R01, which typically supports mature, hypothesis-driven or development-and-validation research projects with a clear plan, milestones, and strong justification for how the proposed approach advances the field.

What does "Clinical Trial Optional" mean for applicants?

"Clinical Trial Optional" means an applicant may include a clinical trial if it fits the proposed work, but a clinical trial is not required to apply under this FOA.

What is the activity category listed for this opportunity?

The activity category is Education and Health.

What CFDA numbers are associated with this FOA?

The CFDA numbers associated with this opportunity are 93.393 and 93.394.

When was this opportunity originally posted, and what closing date is shown?

The original posting date is May 1, 2019, and the original closing date shown is December 10, 2021.

Who is eligible to apply?

Eligibility is broad. It includes state, county, city or township governments; special district governments; independent school districts; public and state-controlled institutions of higher education; private institutions of higher education; federally recognized Native American tribal governments; tribal organizations that are not federally recognized; public housing authorities/Indian housing authorities; nonprofit organizations with or without 501(c)(3) status; for-profit organizations other than small businesses; and small businesses.

Are minority-serving institutions specifically mentioned as eligible?

Yes. The FOA explicitly notes eligibility for Alaska Native and Native Hawaiian Serving Institutions, Asian American Native American Pacific Islander Serving Institutions (AANAPISISs), Hispanic-serving Institutions, Historically Black Colleges and Universities (HBCUs), and Tribally Controlled Colleges and Universities (TCCUs).

Can faith-based or community-based organizations apply?

Yes. Faith-based or community-based organizations are explicitly listed among eligible applicants.

Are U.S. territories and non-U.S. entities eligible?

Yes. The FOA includes regional organizations, U.S. territories or possessions, and non-U.S. (foreign) entities among eligible applicants.

What kinds of teams are best positioned for this FOA?

The FOA is aimed at teams that can bring together radiology or other medical imaging, laboratory biomarker development and validation, digital pathology, and strong computational/data science capabilities, and then show that the combined system improves early identification of aggressive disease while reducing errors and unintended consequences.

What does the FOA suggest competitive applications should demonstrate?

Based on the description, competitive projects typically show a realistic path to clinically meaningful performance, explain how multi-modal data will be aligned and analyzed, and define success using measures that matter to patients and clinicians (for example, fewer unnecessary biopsies, better prediction of aggressive progression, and fewer missed aggressive cancers).