aceMetastasis Extraction From Non-Small Cell Lung Cancer Clinical Notes Using Large Language Model-Based Classification: Retrospective Comparative Study
Background: Identification of metastasis status in non-small cell lung cancer (NSCLC) is a critical part of understanding disease prognosis, treatment courses, trial eligibility, and population-level cancer surveillance. However, metastasis status is inconsistently recorded in structured cancer registry fields, since manual abstraction of clinical notes is often an error-prone process. This challenge highlights an opportunity for leveraging large language models (LLMs) to conduct high-scale metastasis extraction…
LLMs classified metastasis from clinical notes with fairly high accuracy and recovered plausible labels for patients with missing registry fields, which could complement cancer registries.
The fine-tuned encoder may have been limited by inaccuracies in the registry reference standards used for training, and the manually annotated cohort contained only 22 patients.
Evidence
- Peer-reviewedJMIR Cancer2026-10-16
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Truvace Impact Record TRV-2026-1636, v1: “Metastasis Extraction From Non-Small Cell Lung Cancer Clinical Notes Using Large Language Model-Based Classification: Retrospective Comparative Study.” Truvace, 2026-10-17. /record/TRV-2026-1636 (accessed at citation time). sha256 4033327aa1d2eb7e…
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