Perception and Cognition Research to Inform Cancer Image Interpretation (R01 Clinical Trial Optional) | PAR 19 387

Opportunity Information: Apply for PAR 19 387

The Perception and Cognition Research to Inform Cancer Image Interpretation (R01 Clinical Trial Optional) opportunity (PAR-19-387) is a National Institutes of Health (NIH) discretionary grant that supports research aimed at improving how cancer is detected and diagnosed from medical images. The central idea is that the accuracy of radiology and pathology image interpretation is strongly influenced by human perception and cognition, not just by the imaging technology itself. This FOA encourages projects that examine how clinicians and other image observers search, notice, interpret, decide, and sometimes miss cancer-related findings, with the goal of producing evidence-based approaches that reduce diagnostic error and improve decision quality in real clinical or clinically realistic settings.

The scientific focus is on understanding the perceptual and cognitive processes that drive observer performance during cancer image interpretation. In practical terms, the FOA is meant to fund studies that look at things like visual attention, pattern recognition, uncertainty, decision thresholds, workload, fatigue, experience level, training effects, and the influence of context on judgment. It is relevant to both radiology and pathology, where cancer detection often depends on recognizing subtle visual patterns and integrating them with prior knowledge. By targeting the human factors behind interpretation performance, the research supported under this announcement is intended to generate strategies that improve diagnostic accuracy, consistency, and reliability, potentially leading to better patient outcomes through earlier or more precise detection and diagnosis.

The funding mechanism is an R01, meaning it is designed for mature, hypothesis-driven research programs that can deliver substantial, generalizable findings. The announcement is labeled “Clinical Trial Optional,” which signals that applicants may propose a clinical trial if their specific aims require one, but a trial is not required. Projects could therefore range from observational and experimental studies of reader behavior to interventions that test new training methods, workflow adjustments, decision supports, or other approaches intended to measurably improve interpretation performance. While the FOA summary emphasizes basic perceptual and cognitive processes, the practical intent is translational: to use those insights to inform improvements in real-world cancer image interpretation.

Eligibility is broad and intentionally inclusive. In addition to common applicant types such as universities, hospitals, nonprofits, for-profits (other than small businesses), small businesses, and a wide range of government entities (state, county, city/township, special districts, independent school districts, public housing authorities), the FOA explicitly notes eligibility for a variety of institutions and organizations that are often underrepresented in federal research portfolios. These include 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), Tribally Controlled Colleges and Universities (TCCUs), and Indian/Native American tribal governments that are not federally recognized, as well as regional organizations. It also allows eligible federal agencies and faith-based or community-based organizations, and it permits non-U.S. entities (foreign organizations) and U.S. territories or possessions to apply, expanding opportunities for international collaboration or research in diverse care settings.

Administrative details from the source indicate the opportunity was created on 2019-10-02 and lists an original closing date of 2023-01-07. The award ceiling and expected number of awards are not specified in the provided data. The FOA is associated with CFDA (now commonly referenced through Assistance Listing) numbers 93.286, 93.394, and 93.395, reflecting NIH program areas related to cancer and biomedical research funding streams.

Overall, this FOA is best understood as an NIH effort to strengthen cancer diagnosis by treating image interpretation as a high-stakes human performance problem. It supports rigorous research into how observers actually perceive and think when interpreting cancer images, and it seeks interventions or insights that can be used to reduce misses, improve diagnostic decisions, and make interpretation more accurate and dependable across different readers, settings, and patient populations.

• The National Institutes of Health in the education, health sector is offering a public funding opportunity titled "Perception and Cognition Research to Inform Cancer Image Interpretation (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): 93.286, 93.394, 93.395.
• This funding opportunity was created on 2019-10-02.
• Applicants must submit their applications by 2023-01-07. (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 19 387

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