Overview of #SMM4H-HeaRD 2026 – Task 6: Predicting TNM staging from pathology reports
TL;DR
These results suggest that while fine-tuned domain-specific encoders excel at surface-level extraction, larger general-purpose LLMs may be more robust when staging must be inferred from contextual clinical findings.
Abstract
This paper provides an overview of Task 6 from the Social Media Mining for Health/Health Real-World Data shared task (#SMM4H-HeaRD 2026), which focused on predicting TNM staging from pathology reports from TCGA. Seven teams submitted systems spanning fine-tuned clinical encoders, open-source generative LLMs, and closed-source API models. On a straightforward test set, most teams achieved near-perfect F1 scores (average 0 . 993 , 0 . 972 , and 0 . 957 for T, N, and M). However, on a harder tiebreak set where explicit TNM notation was removed and staging had to be inferred from clinical descriptions, performance dropped substantially (average 0 . 725 , 0 . 783 , and 0 . 846 ). Notably, the two teams using large closed-source API models generalized best to the harder set, achieving the highest T and N scores despite not leading on the easy set. These results suggest that while fine-tuned domain-specific encoders excel at surface-level extraction, larger general-purpose LLMs may be more robust when staging must be inferred from contextual clinical findings. All teams surpassed baseline overall performance on both test sets.