Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models

• URL: http://arxiv.org/abs/2512.20633v1
• Date: Mon, 01 Dec 2025 23:56:45 GMT
• Title: Enhancing Lung Cancer Treatment Outcome Prediction through Semantic Feature Engineering Using Large Language Models
• Authors: MunHwan Lee, Shaika Chowdhury, Xiaodi Li, Sivaraman Rajaganapathy, Eric W Klee, Ping Yang, Terence Sio, Liewei Wang, James Cerhan, Nansu NA Zong,
• Abstract summary: We introduce a framework that uses Large Language Models (LLMs) as Goal-oriented Knowledge Curators (GKC)<n>GKC converts laboratory, genomic, and medication data into high-fidelity, task-aligned features.<n>We benchmarked GKC against expert-engineered features, direct text embeddings, and an end-to-end transformer.
• Score: 5.778370321351782
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate prediction of treatment outcomes in lung cancer remains challenging due to the sparsity, heterogeneity, and contextual overload of real-world electronic health data. Traditional models often fail to capture semantic information across multimodal streams, while large-scale fine-tuning approaches are impractical in clinical workflows. We introduce a framework that uses Large Language Models (LLMs) as Goal-oriented Knowledge Curators (GKC) to convert laboratory, genomic, and medication data into high-fidelity, task-aligned features. Unlike generic embeddings, GKC produces representations tailored to the prediction objective and operates as an offline preprocessing step that integrates naturally into hospital informatics pipelines. Using a lung cancer cohort (N=184), we benchmarked GKC against expert-engineered features, direct text embeddings, and an end-to-end transformer. Our approach achieved a mean AUROC of 0.803 (95% CI: 0.799-0.807) and outperformed all baselines. An ablation study further confirmed the complementary value of combining all three modalities. These results show that the quality of semantic representation is a key determinant of predictive accuracy in sparse clinical data settings. By reframing LLMs as knowledge curation engines rather than black-box predictors, this work demonstrates a scalable, interpretable, and workflow-compatible pathway for advancing AI-driven decision support in oncology.

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