Abstract A141: Precision population cancer medicine for pancreatic ductal adenocarcinoma: A framework integrating genomics, artificial intelligence, theranostics, and population health
Melissa Heard, Madison Jones, Srinivasan VijayakumarAbstract
Background:
Pancreatic ductal adenocarcinoma (PDAC) remains among the deadliest malignancies, with a 5-year survival rate of approximately 13%. Advances in genomics, artificial intelligence (AI), liquid biopsy, and theranostics have expanded opportunities for precision oncology; however, implementation remains uneven across populations. Precision Population Cancer Medicine (PPCM) seeks to integrate molecular technologies with population health approaches to enhance equitable cancer care.
Methods:
A systematized narrative review of contemporary literature and emerging clinical evidence was conducted to evaluate applications of genomics, AI-enabled risk prediction, liquid biopsy, molecular profiling, theranostics, and social determinants of health in PDAC prevention, early detection, treatment, and survivorship.
Results:
Evidence supports germline testing, AI-assisted radiomics, liquid biopsy technologies, and molecular profiling for identification of high-risk populations and biologically informed treatment selection. Synthesizing these advances within a Precision Population Cancer Medicine framework highlights opportunities to integrate individual-level precision oncology with population-level risk stratification, resource allocation, and equity-focused implementation strategies. Emerging fibroblast activation protein inhibitor (FAPI)-targeted imaging and therapeutic approaches demonstrate promising applications for disease characterization and precision theranostics in PDAC. Persistent disparities in genomic testing, specialty care access, and clinical trial participation remain substantial barriers, particularly among racial minority and rural populations.
Conclusions:
PPCM provides an emerging framework for integrating molecular diagnostics, AI, theranostics, and population health strategies across the PDAC care continuum. Prospective validation and equity-centered implementation efforts are needed to determine whether these approaches can improve early detection, treatment delivery, and outcomes at a population level.Generative artificial intelligence was used solely to assist with editing and improving the clarity of this abstract. All authors reviewed and approved the final content and are responsible for its accuracy.
Citation Format:
Melissa Heard, Madison Jones, Srinivasan Vijayakumar. Precision population cancer medicine for pancreatic ductal adenocarcinoma: A framework integrating genomics, artificial intelligence, theranostics, and population health [abstract]. In: Proceedings of the AACR Conference on Pancreatic Cancer: New Frontiers in Biology and Therapeutic Development; 2026 Sep 25-28; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(18_Suppl_2):Abstract nr A141.