Abstract:Pathology reports serve as the definitive record for breast cancer staging, yet their unstructured format impedes large-scale data curation. While Large Language Models (LLMs) offer semantic reasoning, their deployment is often limited by high computational costs and hallucination risks. This study introduces a parameter-efficient, multi-task framework for automating the extraction of Tumor-Node-Metastasis (TNM) staging, histologic grade, and biomarkers. We fine-tune a Llama-3-8B-Instruct encoder using Low-Rank Adaptation (LoRA) on a curated, expert-verified dataset of 10,677 reports. Unlike generative approaches, our architecture utilizes parallel classification heads to enforce consistent schema adherence. Experimental results demonstrate that the model achieves a Macro F1 score of 0.976, successfully resolving complex contextual ambiguities and heterogeneous reporting formats that challenge traditional extraction methods including rule-based natural language processing (NLP) pipelines, zero-shot LLMs, and single-task LLM baselines. The proposed adapter-efficient, multi-task architecture enables reliable, scalable pathology-derived cancer staging and biomarker profiling, with the potential to enhance clinical decision support and accelerate data-driven oncology research.
| Comments: | 11 pages, 3 figures and 4 tables in the main manuscript. Additional content, figures and tables are in supplementary material section. 17 pages in total |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.13328 [cs.LG] |
| (or arXiv:2604.13328v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.13328 arXiv-issued DOI via DataCite |
From: Jiahao Shao [view email]
[v1]
Tue, 14 Apr 2026 22:26:30 UTC (336 KB)
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