In August 2023, a team of volunteers led by Trinitia Cannon, MD (third from left), Leda Scearce, CCC-SLP, MM, MS, and Dina Abouelella, MPH, which also included Tammara Watts, MD, PhD (center) and Katharine Ciarrocca, DMD, MSEd, partnered with North Raleigh International Baptist Church and Duke Raleigh Hospital to offer head and neck cancer screenings. Dozens of families from the Cedar Creek Apartment Complex community came out for the free screenings, education, and games, and Duke Raleigh Hospital donated backpacks full of school supplies.
Novel Community Engagement/Research Effort in Head & Neck Cancer Launches
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Members of the DCI Head & Neck Cancer team (in front) take a selfie with members of the Cedar Creek Apartment Complex community in North Raleigh during a public education and screening event. The community includes families from at least seven different countries in Africa, Asia, and the Middle East.
Led by DCI head and neck surgeon Trinitia Cannon, MD, an associate professor in the Department, the project will be the Department’s first community-based participatory research project and the first such head and neck cancer screening and cancer prevention education project in North Carolina.
Evolving Community Research
The Project CHECKERS team will use a mixed methodology, which includes traditional surveys and screenings as well as interviews and focus groups.
One of their community partners will be the Cedar Creek Apartment Complex community in North Raleigh. Many of these families are refugees — from at least seven different countries in Africa, Asia, and the Middle East — who speak Farsi, French, Swahili, Arabic, and other languages. They are building new lives in North Carolina, in a culture and language that is new to many of them. As is the case with many similar communities, their healthcare needs often go unmet.
The investigators believe that, compared to traditional methods, mixed-method research is an improved way to establish a community partnership, highlight gaps in the community’s knowledge and risk perception, and pave the way for successful future health interventions.
According to co-PI Nosayaba (Nosa) Osazuwa-Peters, BDS, MPH, PhD, an associate professor in the Department of Head and Neck Surgery & Communication Sciences, Project CHECKERS takes an important step in improving community engagement.
“Traditional research is very systematic, very top-down. The researchers have knowledge and decide what they believe the community needs. But these outside scientific experts do not know the values, the culture, the knowledge, or the risks inherent in that community,” he explained.
For example, traditional surveys restrict participants to answering either yes or no; for many people, that binary does not tell a complete story.
“Project CHECKERS will help us understand the lived experiences of people in these communities,” added Osazuwa-Peters. “We’ll learn about context, and we’ll learn to ask questions that allow community members to express themselves. We’ll get responses we would never get based on yes or no.”
Building a Partnership
Project CHECKERS kicked off this fall with focus groups and interviews with community members facilitated by Laura Fish, PhD, MPH, assistant professor in Family Medicine and Community Health, Duke University School of Medicine, and program director for the Behavioral Health and Survey Research Core (a DCI shared resource). An advisory board will provide feedback from both clinical and community perspectives.
Lessons learned from these conversations will help the team develop a knowledge and risk factor survey that will be administered during two head and neck cancer screening events with the community in 2024.
The CHECKERS team will also recruit providers outside the department to participate in these events to address other health concerns in the community such as primary care, mental health, and women’s health.
The long-term goal of Project CHECKERS is to show the benefits of tailoring head and neck cancer screening programs to the communities being served, and how that personalization can improve prevention, early detection, and overall survival in high-risk individuals who have limited access to care.
Noted Osazuwa-Peters, “The mixed-methods framework helps us understand not just whether an intervention works, but how, why, and for whom.”
Community Partners
Another plus to mixed-methods research is its appeal to community partners who might otherwise be hesitant to work with researchers.
“The design places a high value on the stories behind the numbers,” explained Cannon, “so these projects are especially attractive to community partners such as faith-based organizations, whose priority is improving practice and outcomes, more so than research and advancing knowledge.”
Project CHECKERS will provide a valuable bridge between Duke and the North Raleigh International Baptist Church (NRIBC), which ministers to a large immigrant community. NRIBC’s Pastor, Patrick Warutere, invited Duke to participate in the church’s inaugural Health and Dignity for All Fair in Raleigh in 2022. Cannon and CHECKERS co-PI Leda Scearce, CCC-SLP, MM, MS, a Duke speech pathologist and director of Community Engagement for the Department of HNS&CS, recruited nurses, medical students, and resident volunteers to provide HNC screenings for the event.
“We immediately felt a kinship with NRIBC’s Pastor Patrick Warutere and his leadership team,” shared Searce. “By the end of the day, we knew we wanted to continue to work together and set up a meeting the following week.”
Duke HNS&CS and the NRIBC team worked closely to develop the research plan and ensure that the goals and expectations of each group were aligned and transparent.
“That relationship with NRIBC has enabled us to incorporate the community’s perspectives into the development of Project CHECKERS,” said Scearce. “Our aim was to amplify the assets and expertise of the community members themselves.”
Cannon anticipates that Project CHECKERS will become a framework for future projects.
“We are looking forward to similar initiatives in hearing health for older adults, right-hemisphere stroke awareness, and more.”
Researchers at the Duke Cancer Institute (DCI) are working with collaborators at leading academic medical centers to explore a faster, less invasive way to detect and analyze head and neck cancers using light and artificial intelligence.In a recent study published in Biophotonics Discovery, a DCI team led by Tuan Vo-Dinh, PhD, partnered with Maie St John, MD, and clinical researchers at the University of California, Los Angeles (UCLA), to demonstrate how a light-based imaging technique combined with machine learning could help distinguish cancerous tissue with high accuracy. The work is now continuing at Johns Hopkins University.Today, diagnosing many cancers such as thyroid and head and neck cancers often relies on pathology or fine needle aspiration (FNA). In an FNA procedure, a thin needle is inserted into a tumor to collect cells, which are then analyzed by a pathologist.“While these approaches are widely used, they can be time-consuming and don’t always give clear or definitive results,” Vo-Dinh said. “In some cases, patients may have to wait weeks for answers, and the accuracy can vary depending on sampling and interpretation.”The collaborative teams used a technology known as Dynamic Optical Contrast Imaging (DOCI), a technique developed at UCLA, which uses laser light to excite tissue. When exposed to this light, different molecules in the tissue emit fluorescence—subtle signals that vary depending on whether tissue is cancerous or healthy. The result is a color-coded “map” of the tissue that reflects its biological properties.“Instead of looking at one spot on the tissue sample, this technique provides spatial information across the entire tissue region monitored,” Vo-Dinh explained. “You can actually see differences in how cancerous and non-cancerous areas respond to light.”These light-based signals are incredibly rich—but also complex. That’s where machine learning comes in. Vo-Dinh’s team applied machine learning algorithms to analyze the complex imaging data generated by DOCI. The researchers trained AI models to recognize subtle patterns in the imaging data and distinguish between different types of thyroid cancer, specifically papillary and follicular thyroid cancers, and to determine whether tissue was cancerous.The results were promising. The machine learning system showed strong agreement with pathological findings, accurately identifying cancerous regions within the tissue samples.“This is an exciting proof of principle demonstration that combining photonics with AI can work together and provide meaningful, reliable answers,” Vo-Dinh said. “The performance we’re seeing so far is very encouraging.”While the research is still in its early stages, the potential implications for patient care are significant.Because this approach directly analyzes the tissue and does not rely on waiting for lab-based assays, it could eventually support faster, point-of-care cancer assessment. Imagine a future where clinicians may be able to use similar tools directly on tissue, potentially even during surgery, to guide decisions in real time.“It could reduce the need for repeat biopsies and long waiting periods,” Vo-Dinh said. “And because the technique is non-invasive, it may also make screening easier and more accessible for patients.”The work underscores the critical importance of collaboration across institutions and disciplines, bringing together clinical expertise, advanced imaging, and data science.“This is a very synergistic partnership,” Vo-Dinh said. “Each group contributes something essential, from clinical insight to optical technology to computational analysis. That kind of complementarity is what drives innovation.”Next steps will focus on expanding and refining the technology, with the goal of enabling real-time, in situ cancer detection.“We’re excited to continue this collaboration and explore how far this approach can go,” Vo-Dinh said.
The Duke Cancer Institute Supportive Care and Survivorship Center is hosting a new Head & Neck Cancer Support Group. This group is designed to provide a structured and supportive environment for patients and caregivers to connect, share experiences, and access peer-informed resources as part of comprehensive, holistic cancer care.The Head & Neck Cancer Support Group will meet on the second Monday of each month. Geoff Vaughan, a licensed medical family therapist with DCI, will facility these sessions. A medical student-based patient navigation component is currently being developed and will launch in spring 2026.This initiative is supported by the Albert Schweitzer Fellowship, North Carolina Chapter (2025–2026), awarded to Rebecca Zasloff and Alie Hunter, medical students at Duke University School of Medicine and members of the Transdisciplinary Otolaryngology Research, Community Health, and Equity (TORCHE) Lab led by Nosayaba Osazuwa-Peters, PhD. The project has also recently received Duke Bass Connections funding for the 2026–2027 academic year, which will support the continuation and expansion of this work.
Researchers at the Duke Cancer Institute (DCI) are working with collaborators at leading academic medical centers to explore a faster, less invasive way to detect and analyze head and neck cancers using light and artificial intelligence.In a recent study published in Biophotonics Discovery, a DCI team led by Tuan Vo-Dinh, PhD, partnered with Maie St John, MD, and clinical researchers at the University of California, Los Angeles (UCLA), to demonstrate how a light-based imaging technique combined with machine learning could help distinguish cancerous tissue with high accuracy. The work is now continuing at Johns Hopkins University.Today, diagnosing many cancers such as thyroid and head and neck cancers often relies on pathology or fine needle aspiration (FNA). In an FNA procedure, a thin needle is inserted into a tumor to collect cells, which are then analyzed by a pathologist.“While these approaches are widely used, they can be time-consuming and don’t always give clear or definitive results,” Vo-Dinh said. “In some cases, patients may have to wait weeks for answers, and the accuracy can vary depending on sampling and interpretation.”The collaborative teams used a technology known as Dynamic Optical Contrast Imaging (DOCI), a technique developed at UCLA, which uses laser light to excite tissue. When exposed to this light, different molecules in the tissue emit fluorescence—subtle signals that vary depending on whether tissue is cancerous or healthy. The result is a color-coded “map” of the tissue that reflects its biological properties.“Instead of looking at one spot on the tissue sample, this technique provides spatial information across the entire tissue region monitored,” Vo-Dinh explained. “You can actually see differences in how cancerous and non-cancerous areas respond to light.”These light-based signals are incredibly rich—but also complex. That’s where machine learning comes in. Vo-Dinh’s team applied machine learning algorithms to analyze the complex imaging data generated by DOCI. The researchers trained AI models to recognize subtle patterns in the imaging data and distinguish between different types of thyroid cancer, specifically papillary and follicular thyroid cancers, and to determine whether tissue was cancerous.The results were promising. The machine learning system showed strong agreement with pathological findings, accurately identifying cancerous regions within the tissue samples.“This is an exciting proof of principle demonstration that combining photonics with AI can work together and provide meaningful, reliable answers,” Vo-Dinh said. “The performance we’re seeing so far is very encouraging.”While the research is still in its early stages, the potential implications for patient care are significant.Because this approach directly analyzes the tissue and does not rely on waiting for lab-based assays, it could eventually support faster, point-of-care cancer assessment. Imagine a future where clinicians may be able to use similar tools directly on tissue, potentially even during surgery, to guide decisions in real time.“It could reduce the need for repeat biopsies and long waiting periods,” Vo-Dinh said. “And because the technique is non-invasive, it may also make screening easier and more accessible for patients.”The work underscores the critical importance of collaboration across institutions and disciplines, bringing together clinical expertise, advanced imaging, and data science.“This is a very synergistic partnership,” Vo-Dinh said. “Each group contributes something essential, from clinical insight to optical technology to computational analysis. That kind of complementarity is what drives innovation.”Next steps will focus on expanding and refining the technology, with the goal of enabling real-time, in situ cancer detection.“We’re excited to continue this collaboration and explore how far this approach can go,” Vo-Dinh said.
The Duke Cancer Institute Supportive Care and Survivorship Center is hosting a new Head & Neck Cancer Support Group. This group is designed to provide a structured and supportive environment for patients and caregivers to connect, share experiences, and access peer-informed resources as part of comprehensive, holistic cancer care.The Head & Neck Cancer Support Group will meet on the second Monday of each month. Geoff Vaughan, a licensed medical family therapist with DCI, will facility these sessions. A medical student-based patient navigation component is currently being developed and will launch in spring 2026.This initiative is supported by the Albert Schweitzer Fellowship, North Carolina Chapter (2025–2026), awarded to Rebecca Zasloff and Alie Hunter, medical students at Duke University School of Medicine and members of the Transdisciplinary Otolaryngology Research, Community Health, and Equity (TORCHE) Lab led by Nosayaba Osazuwa-Peters, PhD. The project has also recently received Duke Bass Connections funding for the 2026–2027 academic year, which will support the continuation and expansion of this work.
