Recent advances on AI-driven decoding of tertiary lymphoid structures in precision cancer immunotherapy

Tertiary lymphoid structures (TLS) are organized ectopic immune aggregates within the tumor microenvironment and have been associated with prognosis and response to immunotherapy across multiple cancer types. However, their clinical interpretation cannot be reduced to a simple presence-or-absence assessment, and conventional histopathological evaluation is limited in reproducibility, scalability, and functional resolution. Recent advances in artificial intelligence (AI), digital pathology, spatial omics, and multiplex imaging provide new opportunities to quantify TLS morphology, maturity, spatial distribution, cellular composition, and therapy-associated changes. In this review, we summarize recent AI-based approaches for TLS detection, segmentation, maturity classification, multimodal integration, and non-invasive prediction, while also discussing their validation requirements and current limitations. We further examine TLS maturity as a functional continuum, highlighting the distinction between functionally active mature TLS and morphologically mature but functionally impaired "pseudo-mature" TLS. In addition, we discuss how proximal, distal, and therapy-remodeled TLS may have distinct biological and clinical implications. Finally, we outline a conceptual "quantify-decode-intervene-reassess" framework that links AI-assisted TLS assessment with spatial validation and emerging synthetic-immunology strategies. We emphasize that AI-derived TLS scores and the proposed TLS Functional Vitality Index remain investigational frameworks that require rigorous analytical, biological, and clinical validation before clinical deployment.