Multiphysical Tumor Tissue Modeling for Improved Multimodal Sensor-Based Diagnostics.

Area of Science:

• Biomedical Engineering
• Oncology
• Medical Imaging

Background:

• Accurate tumor resection in oncological surgery is vital but challenged by tissue heterogeneity and visual limitations in endoscopy.
• Multi-physical endoscopic sensors (impedance, waterflow elastography) offer potential for improved tissue differentiation by capturing distinct tissue properties.
• Effective sensor fusion requires advanced machine learning and coupled multimodal training data, which is currently scarce.

Purpose of the Study:

• To develop a comprehensive mathematical tissue model for generating multimodal synthetic data.
• To simulate tumor-induced alterations in tissue, including extracellular matrix (ECM) changes and cellular modifications.
• To address the scarcity of coupled multimodal datasets for training machine learning algorithms in oncological surgery.

Main Methods:

• Developed a mathematical tissue model simulating tumor cell density effects on ECM and cells.
• Integrated tumor-induced alterations like cytoskeletal remodeling and ECM cross-linking.
• Generated a multimodal synthetic dataset using parameter identification from real impedance and elastography measurements for transfer learning.

Main Results:

• The generated synthetic data closely mimicked real tissue impedance and elastography measurements.
• Validation confirmed the synthetic data's accuracy in reflecting known tumor cell densities.
• The model successfully generated multimodal synthetic data suitable for neural network transfer learning.

Conclusions:

• Synthetic data generation using mathematical modeling is a viable solution for overcoming multimodal dataset scarcity.
• Sensor fusion, powered by machine learning trained on synthetic data, can significantly enhance tissue differentiation in oncological procedures.
• This approach provides a foundation for multimodal diagnostics, aiming to improve intraoperative decision-making and patient outcomes.