Researchers from the ECOG-ACRIN Cancer Research Group (ECOG-ACRIN) have applied AI-driven processes for detecting tertiary lymphoid structures (TLS) in thousands of digital images of melanoma tumor tissue, significantly enhancing TLS identification and survival predictions for operable stage III/IV patients. The presence of TLS, a key biomarker for better prognosis and improved survival, is not yet a standard part of patients' pathology reports, and manual detection is labor-intensive and can be variable. Lead investigators Ahmad A. Tarhini, MD, PhD , and Xuefeng Wang, PhD , will present the new approach at the American Association for Cancer Research 2025 Annual Meeting in Chicago.
"Our efforts reveal the potential of open-source AI tools to transform how we predict survival and immunotherapy benefits by detecting critical immune structures like TLS with unprecedented ease and accuracy," said Dr. Tarhini, professor and senior member, cutaneous oncology and immunology, at the Moffitt Cancer Center and Research Institute in Tampa, Florida.
The study retrospectively analyzed thousands of archived digital images coupled with corresponding RNA sequencing data from 376 patients with advanced, high-risk melanoma, linking TLS presence to significantly better overall survival. The cohort had participated in a landmark US cooperative group trial led by ECOG-ACRIN called E1609 that tested immune check point blockade and cytokine therapy in high-risk melanoma ( Tarhini A. J Clin Oncol. February 2020).
