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        <title>URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow</title>
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    <name type="personal">
        <namePart type="given">Natalia</namePart>
        <namePart type="family">Madrueño</namePart>
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    <name type="personal">
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            <title>Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks</title>
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            <namePart type="given">Guillermo</namePart>
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            <namePart type="given">Graciela</namePart>
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    <abstract>TNM cancer staging is a critical process for characterizing tumor burden and guiding clinical decisions. Nevertheless, its automated extraction remains challenging due to the unstructured and heterogeneous nature of free-text pathology reports. This paper describes the participation of the URJC-Team in Task 6 of the Social Media Mining for Health/Health Real-World Data (#SMM4H-HeaRD) 2026 Shared Tasks. It focuses on predicting TNM staging from pathology reports. The proposed workflow combines hand-crafted regular expressions with a Large Language Model (LLM). First, explicit TNM mentions are extracted using rule-based patterns. Then, any stage not recovered by these rules is inferred by an LLM. Overall, the proposal provides competitive results across all official shared-task phases.</abstract>
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            <start>133</start>
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%0 Conference Proceedings
%T URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow
%A Madrueño, Natalia
%A Pérez, Jose Walter Hernández
%A Fernández, Rubén R.
%A Montalvo, Soto
%Y Lopez-Garcia, Guillermo
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, United States
%@ 979-8-89176-432-3
%F madrueno-etal-2026-urjc
%X TNM cancer staging is a critical process for characterizing tumor burden and guiding clinical decisions. Nevertheless, its automated extraction remains challenging due to the unstructured and heterogeneous nature of free-text pathology reports. This paper describes the participation of the URJC-Team in Task 6 of the Social Media Mining for Health/Health Real-World Data (#SMM4H-HeaRD) 2026 Shared Tasks. It focuses on predicting TNM staging from pathology reports. The proposed workflow combines hand-crafted regular expressions with a Large Language Model (LLM). First, explicit TNM mentions are extracted using rule-based patterns. Then, any stage not recovered by these rules is inferred by an LLM. Overall, the proposal provides competitive results across all official shared-task phases.
%U https://aclanthology.org/2026.smm4h-1.22/
%P 133-138

Markdown (Informal)

[URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow](https://aclanthology.org/2026.smm4h-1.22/) (Madrueño et al., SMM4H 2026)

• URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow (Madrueño et al., SMM4H 2026)

ACL

• Natalia Madrueño, Jose Walter Hernández Pérez, Rubén R. Fernández, and Soto Montalvo. 2026. URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow. In Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks, pages 133–138, San Diego, United States. Association for Computational Linguistics.