FACT: Foundation Model for Assessing Cancer Tissue Margins with Mass Spectrometry
Journal:
arXiv
Published Date:
Apr 15, 2025
Abstract
Purpose: Accurately classifying tissue margins during cancer surgeries is
crucial for ensuring complete tumor removal. Rapid Evaporative Ionization Mass
Spectrometry (REIMS), a tool for real-time intraoperative margin assessment,
generates spectra that require machine learning models to support clinical
decision-making. However, the scarcity of labeled data in surgical contexts
presents a significant challenge. This study is the first to develop a
foundation model tailored specifically for REIMS data, addressing this
limitation and advancing real-time surgical margin assessment. Methods: We
propose FACT, a Foundation model for Assessing Cancer Tissue margins. FACT is
an adaptation of a foundation model originally designed for text-audio
association, pretrained using our proposed supervised contrastive approach
based on triplet loss. An ablation study is performed to compare our proposed
model against other models and pretraining methods. Results: Our proposed model
significantly improves the classification performance, achieving
state-of-the-art performance with an AUROC of $82.4\% \pm 0.8$. The results
demonstrate the advantage of our proposed pretraining method and selected
backbone over the self-supervised and semi-supervised baselines and alternative
models. Conclusion: Our findings demonstrate that foundation models, adapted
and pretrained using our novel approach, can effectively classify REIMS data
even with limited labeled examples. This highlights the viability of foundation
models for enhancing real-time surgical margin assessment, particularly in
data-scarce clinical environments.
