---
title: "URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction"
description: "The paper tackles automatic TNM cancer staging from free-text pathology reports for Task 6 of the… 14 references, cited by 1 paper here."
authors: ["Natalia Madrueño", "Jose Walter Hernández Pérez", "Rubén R. Fernández", "Soto Montalvo"]
year: 2026
venue: "Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks"
source: "https://aclanthology.org/2026.smm4h-1.22/"
doi: "10.18653/v1/2026.smm4h-1.22"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/madrueno-2026-urjc-team-smm4h-heard-2026/"
---
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# URJC-Team at #SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow

**Authors:** Natalia Madrueño, Jose Walter Hernández Pérez, Rubén R. Fernández and [Soto Montalvo](https://md.research.elamur.ai/free/en/authors/soto-montalvo/) · **Year:** 2026 · **Venue:** [Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks](https://md.research.elamur.ai/free/en/venues/smm4h/) ([Social Media Mining for Health Research and Applications Workshop and Shared Tasks 2026](https://md.research.elamur.ai/free/en/venues/smm4h/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.smm4h-1.22/) · [PDF](https://aclanthology.org/2026.smm4h-1.22.pdf) · [DOI 10.18653/v1/2026.smm4h-1.22](https://doi.org/10.18653/v1/2026.smm4h-1.22) · [OpenAlex](https://openalex.org/W7166891203)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary
The paper tackles automatic TNM cancer staging from free-text pathology reports for Task 6 of the #SMM4H-HeaRD 2026 Shared Tasks. Systems must predict the T (T1–T4), N (N0–N3) and M (M0–M1) components independently, following the AJCC 7th edition. The data come from TCGA pathology reports covering about 7,000 patients and 23 cancer types. Labels are strongly imbalanced, and many values are missing.

The URJC-Team built a two-stage pipeline. Hand-written regular expressions first capture explicitly stated staging codes. Any component the rules miss is passed to Qwen3.5-27B, used either zero-shot or after supervised fine-tuning with QLoRA. On validation, the fine-tuned variants scored best, beating several baselines including BB-TEN, logistic regression and a fully fine-tuned BioClinical-ModernBERT-large. The regex layer did not reduce accuracy and lowered inference cost.

The official evaluation set mostly contained explicit stage codes, and all submissions scored perfectly. On the harder post-evaluation set, explicit codes were almost absent, yet the system still clearly beat the baseline. This shows that cheap rules plus an LLM fallback can work both when staging is stated outright and when it must be inferred.

## Key points
- Training split: 6,774 reports, with many labels missing; for example, M is absent in 42% of reports and M1 makes up only 4%.
- Validation phase: LLM SFT and Regex + LLM SFT both reached 0.82 Macro-F1 Stage and 0.92 Micro-F1. For comparison, LLM ZS scored 0.78, SML FFT 0.76, BB-TEN 0.71 and Regex alone 0.34 (Macro-F1 Stage).
- Evaluation phase: the regex rules resolved 99.3% of T, 99.4% of N and 96.5% of M cases. All three submissions scored 1.00 on every metric, against a baseline Micro-F1 of 0.95.
- Post-evaluation phase: only 0.2% of reports carried explicit TNM codes. Regex + LLM SFT achieved 0.86 Macro-F1 Stage and 0.93 Micro-F1 (baseline 0.53 / 0.71), with a perfect 1.00 Macro-F1 on M.
- Stated limitations: high compute demands of the LLM, dependence on labeled data for SFT, evaluation limited to TCGA reports, and no explanations or confidence flags for predictions.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: 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. Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{madrueno2026urjc,
  title = {{URJC-Team at \#SMM4H-HeaRD 2026: TNM Stage Extraction with a Regex-LLM Workflow}},
  author = {Natalia Madrueño, Jose Walter Hernández Pérez, Rubén R. Fernández and Soto Montalvo},
  booktitle = {Proceedings of the 11th Social Media Mining for Health Research and Applications (SMM4H-HeaRD 2026) Workshop and Shared Tasks},
  year = {2026},
  doi = {10.18653/v1/2026.smm4h-1.22},
  url = {https://doi.org/10.18653/v1/2026.smm4h-1.22}
}
```

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## References

14 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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8. Ruonan Jin, Chao Ling, Yixuan Hou, Yuhan Sun, Ning Li, Jiefei Han, Jin Sheng, Qizhao Wang, Yuepeng Liu, Shen Zheng, Xingyu Ren, Chiyu Chen, Jue Wang, and Cheng Li. 2026. Augmenting large language model with prompt engineering and supervised fine-tuning in non-small cell lung cancer tumor-node-metast… [find](https://scholar.google.com/scholar?q=Ruonan+Jin%2C+Chao+Ling%2C+Yixuan+Hou%2C+Yuhan+Sun%2C+Ning+Li%2C+Jiefei+Han%2C+Jin+Sheng%2C+Qizhao+Wang%2C+Yuepeng+Liu%2C+Shen+Zheng%2C+Xingyu+Ren%2C+Chiyu+Chen%2C+Jue+Wang%2C+and+Cheng+Li%2E+2026%2E+Augmenting+large+language+mode)
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11. Guillermo Lopez-Garcia, Jose Miguel Acitores Cortina, Jacob Berkowitz, Joey Chan, Ganesh Chandrasekar, Sumon Kanti Dey, Ivan Flores Amaro, Fernando Gallego, Lauren Gryboski, Ari Z. Klein, Martin Krallinger, Salvador Lima-López, Tomohiro Nishiyama, Lisa Raithel, Ahmad Rezaie Mianroodi, Roland Roller,… [find](https://scholar.google.com/scholar?q=Guillermo+Lopez%2DGarcia%2C+Jose+Miguel+Acitores+Cortina%2C+Jacob+Berkowitz%2C+Joey+Chan%2C+Ganesh+Chandrasekar%2C+Sumon+Kanti+Dey%2C+Ivan+Flores+Amaro%2C+Fernando+Gallego%2C+Lauren+Gryboski%2C+Ari+Z%2E+Klein%2C+Martin+Krall)
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13. Hyung Jun Park, Namu Park, Jang Ho Lee, Myeong Geun Choi, Jin-Sook Ryu, Min Song, and Chang-Min Choi. 2022. Automated extraction of information of lung cancer staging from unstructured reports of pet-ct interpretation: natural language processing with deep-learning. BMC medical informatics and decis… [find](https://scholar.google.com/scholar?q=Hyung+Jun+Park%2C+Namu+Park%2C+Jang+Ho+Lee%2C+Myeong+Geun+Choi%2C+Jin%2DSook+Ryu%2C+Min+Song%2C+and+Chang%2DMin+Choi%2E+2022%2E+Automated+extraction+of+information+of+lung+cancer+staging+from+unstructured+reports+of+pet%2D)
14. You are an expert oncology pathologist . [find](https://scholar.google.com/scholar?q=You+are+an+expert+oncology+pathologist+%2E)

*Updated 1 November 2026*