Towards transparent and interpretable screening: multi-biofluid FTIR spectroscopy with LLM-Augmented explainability for pancreatic cancer detection.

Early detection of pancreatic cancer remains a critical challenge in oncology, with current diagnostic methods often failing to identify the disease until advanced stages. However, diagnostic accuracy alone may be insufficient for clinical adoption as regulatory frameworks and clinical workflows increasingly demand transparent, interpretable AI systems. This study investigates Fourier Transform Infrared (FTIR) spectroscopy combined with machine learning for non-invasive pancreatic cancer detection using urine and blood biofluids, augmented by a language-model-assisted transparency framework to bridge spectral feature attributions and biochemical interpretation. Five datasets were evaluated: urine ATR-FTIR (61.7% balanced accuracy), urine transmission FTIR (74.8%), filtered blood (<10 kDa; 89.8%), and two matched urine-blood fusion datasets. Transmission-mode urine combined with filtered blood achieved the highest performance (96.9% balanced accuracy), exceeding either biofluid alone. To support transparency, we developed an LLM-augmented explainability pipeline incorporating Monte Carlo Tree Search (MCTS) for structured hypothesis exploration, a curated retrieval-augmented knowledge base (RAG), and reliability-gated explanations that acknowledge disagreement between feature attribution methods. Explainability methods showed substantial disagreement (mean Spearman ρ = 0.23-0.28), motivating a tiered strategy: wavenumber-level interpretation when methods agree (ρ ≥ 0.3, with knowledge base verification) and zone-level interpretation otherwise. These results highlight both the potential and current limitations of transparent spectroscopic diagnostics.