FACT: Foundation Model for Assessing Cancer Tissue Margins with Mass Spectrometry

• URL: http://arxiv.org/abs/2504.11519v1
• Date: Tue, 15 Apr 2025 16:36:03 GMT
• Title: FACT: Foundation Model for Assessing Cancer Tissue Margins with Mass Spectrometry
• Authors: Mohammad Farahmand, Amoon Jamzad, Fahimeh Fooladgar, Laura Connolly, Martin Kaufmann, Kevin Yi Mi Ren, John Rudan, Doug McKay, Gabor Fichtinger, Parvin Mousavi,
• Abstract summary: 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.<n>Results: Our proposed model significantly improves the classification performance, achieving state-of-the-art performance with an AUROC of $82.4% pm 0.8$.
• Score: 1.0183055506531902
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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