Abstract 3684: Using key informants to guide community outreach for cancer topics and areas of focus for Filipino communities

Since 2003, the University of Hawai‘i Cancer Center (UHCC) and the University of Guam (UoG) have worked in partnership to explore health disparities/inequalities for different Pacific Island Populations (PIP). Investigators explore health behaviors and sources of health disparities and equity among Micronesian and Filipino community members in Hawai‘i, Guam, and the neighboring U.S. Associated Pacific Islands (USAPI) through the Pacific Island Partnership for Cancer Health Equity (PIPCHE). The Community Outreach Core of the partnership works towards objectives that include assisting and promoting PIPCHE research, ensuring inclusion PIP and Filipino community perspectives in research, and building healthcare providers’ competencies. This work aims to directly address the disproportionately high rates of cancer incidence and mortality found amongst these groups in Hawai‘i and Guam. Currently, few cancer control initiatives are designed to specifically address Micronesians and Filipinos, which comprise 40% of Hawai‘i’s population and 70% of Guam’s population. Colorectal cancer is the second leading cause of death for men and the third leading cause of death for Filipinos in Hawai‘i. Also, Micronesians and Filipinos are highly underrepresented among cancer researchers and cancer health care professionals; culturally-grounded approaches to achieve parity in cancer control are sorely needed which remains a focus of the COC. To address the needs of the Filipino community in Hawai‘i, we conducted five semi-structured interviews with Filipino community members to facilitate community engagement, build relationships, and direct future areas for cancer control. Another goal was to establish a relationship-building process to recruit members for the COC Outreach Advisory Council to guide future community efforts. Participants ranged in age; however, all five identified as female were born in various parts of the Philippines, and migrated to Hawai'i at different ages. Some findings from the interviews included the need for culturally tailored and translated cancer materials and resources, a charge to focus on colorectal and breast cancer initiatives, and provide translated health communications utilizing local radio and faith-based organizations. These members were then asked to serve on our Outreach Advisory Council for a period of 5 years to help shape the COC’s efforts towards community engagement with the Filipino community. These planned community-focused efforts should be modeled to ensure shared community-based decision-making for this minority population.

Citation Format: Mark Lee Willingham, Kevin Cassel, Angela Sy, Munirih R. Ta'afaki, Reyna Bodnar, Lilnabeth P. Somera, Tressa P. Diaz, Angelina G. Mummert, Harmony C. Palaganas. Using key informants to guide community outreach for cancer topics and areas of focus for Filipino communities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3684.

The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai'i's multiethnic population

Skin cancer remains the most commonly diagnosed cancer in the USA with more than 1 million new cases each year. Melanomas account for about 1% of all skin cancers and most skin cancer deaths. Multiethnic individuals whose skin is pigmented underestimate their risk for skin cancers and melanomas and may delay seeking a diagnosis. The use of artificial intelligence may help improve the diagnostic precision of dermatologists/physicians to identify malignant lesions. To validate our artificial intelligence's efficiency in distinguishing between images, we utilized 50 images obtained from our International Skin Imaging Collaboration dataset (n = 25) and pathologically confirmed lesions (n = 25). We compared the ability of our artificial intelligence to visually diagnose these 50 skin cancer lesions with a panel of three dermatologists. The artificial intelligence model better differentiated between melanoma vs. nonmelanoma with an area under the curve of 0.948. The three-panel member dermatologists correctly diagnosed a similar number of images (n = 35) as the artificial intelligence program (n = 34). Fleiss' kappa (ĸ) score for the raters and artificial intelligence indicated fair (0.247) agreement. However, the combined result of the dermatologists panel with the artificial intelligence assessments correctly identified 100% of the images from the test data set. Our artificial intelligence platform was able to utilize visual images to discriminate melanoma from nonmelanoma, using de-identified images. The combined results of the artificial intelligence with those of the dermatologists support the use of artificial intelligence as an efficient lesion assessment strategy to reduce time and expense in diagnoses to reduce delays in treatment.