Respirology (Carlton, Vic.) · 2026
Few-Shot Lung Cancer Classification via Electronic Nose Using Large Language Models: A Multicentre Prospective Study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Lee Meng-Rui, Huang Chien-Chi, Sun Joyce Yue, Lin Chang-Ru, Lin Wen-Yuan, Chi Nai-Hui, Tang Kea-Tiong, Wang Jann-Yuan, Ho Chao-Chi, Shih Jin-Yuan, Yu Chong-Jen
Аннотация
BACKGROUND AND OBJECTIVE: Electronic Nose (eNose) breathprints are promising non-invasive lung cancer diagnostic tools, but cross-site validation and adaptation remain barriers to clinical applications. It remains unknown whether a natural language processing-pretrained large language model (LLM) can enable few-shot, site-specific classification of lung cancer using eNose breathprints.
METHODS: We collected eNose breathprints of lung cancer and non-lung cancer patients from two medical centres in Taiwan. A GPT-2-backbone LLM with parameter-efficient adaptation was compared with convolutional neural networks (CNN) trained from scratch or pretrained on CIFAR-100. Few-shot protocols (2-6 shots per class) and full-data training were evaluated.
RESULTS: We collected 432 eNose breathprints from two sites (S1 and S2). With 6 labelled samples per class (6 shots), LLM achieved an area under the curve (AUC) of 0.79 (95% CI: 0.71-0.87), sensitivity of 0.74 (0.63-0.83), and specificity of 0.77 (0.67-0.87) on S1. On S2, it achieved an AUC of 0.76 (0.69-0.82), sensitivity of 0.77 (0.69-0.84), and specificity of 0.61 (0.51-0.70). LLM outperforms scratch CNN models (S1; AUC: 0.44, p = 0.0002) (S2; AUC: 0.63, p = 0.0198) and CNN pretrained on CIFAR-100 images (S1; AUC: 0.57, p = 0.0100) and (S2; AUC: 0.61, p = 0.0248). LLM or a CNN model trained on the source site fails to improve performance after transferring to the target site for fine-tuning; for the LLM, performance even deteriorates.
CONCLUSION: Our study demonstrates the potential of pretrained LLMs for few-shot lung cancer classification in a real-world mixed clinical cohort, reducing dependence on large training datasets.
METHODS: We collected eNose breathprints of lung cancer and non-lung cancer patients from two medical centres in Taiwan. A GPT-2-backbone LLM with parameter-efficient adaptation was compared with convolutional neural networks (CNN) trained from scratch or pretrained on CIFAR-100. Few-shot protocols (2-6 shots per class) and full-data training were evaluated.
RESULTS: We collected 432 eNose breathprints from two sites (S1 and S2). With 6 labelled samples per class (6 shots), LLM achieved an area under the curve (AUC) of 0.79 (95% CI: 0.71-0.87), sensitivity of 0.74 (0.63-0.83), and specificity of 0.77 (0.67-0.87) on S1. On S2, it achieved an AUC of 0.76 (0.69-0.82), sensitivity of 0.77 (0.69-0.84), and specificity of 0.61 (0.51-0.70). LLM outperforms scratch CNN models (S1; AUC: 0.44, p = 0.0002) (S2; AUC: 0.63, p = 0.0198) and CNN pretrained on CIFAR-100 images (S1; AUC: 0.57, p = 0.0100) and (S2; AUC: 0.61, p = 0.0248). LLM or a CNN model trained on the source site fails to improve performance after transferring to the target site for fine-tuning; for the LLM, performance even deteriorates.
CONCLUSION: Our study demonstrates the potential of pretrained LLMs for few-shot lung cancer classification in a real-world mixed clinical cohort, reducing dependence on large training datasets.