Domain adaptation and self-supervised learning for surgical margin detection.

Area of Science:

• Surgical Oncology
• Analytical Chemistry
• Machine Learning

Background:

• Breast-conserving surgery often requires revision due to residual tumor, impacting patient outcomes.
• The iKnife (Intelligent Knife) uses mass spectrometry to provide real-time margin assessment from surgical smoke metabolites.
• Developing accurate iKnife models for breast cancer is challenging due to limited, sensitive data collection.

Purpose of the Study:

• To develop a robust iKnife breast cancer recognition model despite data limitations.
• To leverage self-supervised learning and domain adaptation for improved iKnife data analysis.
• To reduce the incidence of positive margins and subsequent revision surgeries in breast cancer treatment.

Main Methods:

• Utilized self-supervised learning on weakly labeled data to extract general iKnife metabolite features.
• Applied domain adaptation to transfer knowledge from a more accessible cancer type (skin) to breast cancer data.
• Investigated hyper-parameter effects on classifier performance using datasets of skin and breast tissue burns.

Main Results:

• Achieved statistically significant improvements over baseline models (p < 0.0001).
• The two-step method demonstrated high performance with 92% accuracy, 88% sensitivity, and 92% specificity.
• Successfully applied domain transfer for iKnife REIMS data, compensating for limited breast sample size.

Conclusions:

• This is the first demonstration of domain transfer for iKnife REIMS data in cancer classification.
• Self-supervised learning and domain adaptation effectively overcome data limitations in training iKnife breast cancer classifiers.
• Future work includes validating performance with more breast samples and expanding to other cancer types.