Multiphysical Tumor Tissue Modeling for Improved Multimodal Sensor-Based Diagnostics

Accurate differentiation between tumorous and healthy tissue is critical in oncological surgeries to ensure complete tumor resection while minimizing the loss of healthy tissue. However, tissue heterogeneity and visual impairments inherent in endoscopic procedures significantly limit the accuracy of current visual tissue examination. The use of multi-physical endoscopic sensors and subsequent fusion offers a promising solution. Impedance and waterflow elastography sensors are able to capture distinct electrical and mechanical properties of bladder tissue. Nonetheless, effective fusion of the two sensors requires advanced machine learning algorithms for pattern recognition, necessitating coupled multimodal training data. Addressing the scarcity of coupled multimodal datasets, we develop a comprehensive mathematical tissue model that simulates the effects of varying tumor cell densities on the extracellular matrix (ECM) and individual cells. This model integrates tumor-induced alterations such as cytoskeletal remodeling, ECM cross-linking, and changes in fluid content, thereby generating a multimodal synthetic dataset that closely mimics real tissue behavior. Parameter identification was performed using a combination of real impedance and elastography measurements, enabling the generation of synthetic data that is suitable for transfer learning of neural networks. Validation against real-world measurements demonstrated that the synthetic data closely matched the impedance and elastography measurements corresponding to known tumor cell densities. These findings highlight the potential of synthetic data and sensor fusion in enhancing tissue differentiation and classification. Our approach provides a foundation for multimodal diagnostics, improving intraoperative decision-making and patient outcomes. Future work will focus on refining the model to better capture biological variability and exploiting the generated dataset for pretraining advanced sensor fusion algorithms.