A roadmap for breast cancer microwave hyperthermia treatment planning and experimental systems

Abstract

Breast cancer affects approximately 2.5 million women each year and can be fatal if not treated correctly. However, with proper treatment, survival rates are very high. Common treatments include invasive surgical procedures such as lumpectomy or mastectomy, and non-surgical methods like radiotherapy, chemotherapy, and other anti-cancer agents. Enhancing the efficiency of these treatments can mitigate the economic and psychological impacts on patients. Studies have shown that artificial hyperthermia, which involves elevating the temperature in cancerous regions, can enhance the effectiveness of these modalities. Microwave breast hyperthermia (MH) aims to raise the temperature at the tumor site above normal levels. During this procedure, unwanted hotspots can occur, and the main goal of MH is to avoid these while achieving the necessary temperature at the tumor. The specific absorption rate (SAR), which measures the absorbed heat energy per kilogram of breast tissue, needs to be carefully controlled. The design of the MH applicator is crucial for focusing energy on the target effectively. Despite variations in hyperthermia treatment planning (HTP) for each patient, the MH applicator must be effective across different breast models and tumor types. The optimization and predictive modeling of temperature-dependent dielectric properties in microwave hyperthermia treatments, focusing primarily on breast cancer is investigated. This research aims to enhance the efficacy and precision of hyperthermia therapy through a combination of computational simulations, empirical data analysis, and deep learning techniques. This study is a comprehensive exploration of microwave hyperthermia treatment planning for breast cancer, focusing particularly on the critical consideration of temperature-dependent dielectric properties (TD-DP) within this context. In addition, an experimental study was conducted to realize computational analysis. It delves into multifaceted aspects of microwave hyperthermia treatment, spanning from the optimization of antenna parameters to the prediction of electromagnetic distribution through innovative methodologies like the U-Net architecture. One of the central inquiries is the optimization of antenna parameters concerning temperature-dependent dielectric properties. This study delves into the intricacies of how variations in these properties can influence treatment outcomes and efficacy. By analyzing these relationships, this thesis aims to establish optimized antenna configurations that maximize treatment precision and effectiveness. Deep learning, particularly convolutional neural networks (CNN), emerges as a powerful tool within this framework. By leveraging CNNs, this thesis investigates methods to use as a preliminary step of hyperthermia antenna excitation parameter selection. This integration of cutting-edge artificial intelligence techniques holds promise for streamlining and automating aspects of treatment planning, thereby potentially reducing human error and enhancing overall efficiency. Particularly, the U-Net model's potential is studied in automating the generation of electric field distribution of a particular dielectric distribution such as the breast tissue. By harnessing the capabilities of artificial intelligence, particularly in image analysis and processing, it aims to develop more robust and efficient methodologies for treatment planning. The integration of the U-Net model represents a significant advancement in this regard, promising to streamline processes and enhance treatment precision. To verify the performed computational simulations, an experimental microwave hyperthermia system was built. A circular array of 12 dipole antennas was installed in this system to experiment on tissue-mimicking phantom to gather information on microwave hyperthermia treatment system. Therefore, a significant amount of information on microwave hyperthermia is gathered through this experiment. Ultimately, the overarching objective of the thesis is to advance microwave hyperthermia treatment planning for breast cancer by improving both precision and efficacy. By synthesizing insights from diverse disciplines such as electromagnetics and deep learning, this thesis seeks to push the boundaries of current practices and pave the way for more effective treatment strategies. Through its meticulous analysis and innovative approaches, the thesis contributes valuable knowledge and methodologies to the ongoing quest for improved cancer therapies. To achieve that, COMSOL Multiphysics software is utilized to simulate the electromagnetic and thermal behavior of breast tissue during hyperthermia treatment. These simulations consider both constant and temperature-dependent dielectric properties. Empirical data is collected using phantoms that mimic the dielectric properties of breast tissue. Temperature distributions are recorded and compared with simulated results to validate the models. U-Net architecture, an encoder-decoder model, is used to predict electromagnetic field distributions, significantly reducing the computational workload and enhancing the accuracy of treatment planning. This research underscores the importance of optimizing antenna configurations to achieve targeted heating while minimizing damage to surrounding healthy tissues. Variations in tissue properties with temperature are crucial for effective hyperthermia treatment, and modeling these changes can lead to better treatment protocols. Despite the promising results, the transition of high-precision hyperthermia into clinical practice faces challenges such as technical complexities, high computational costs, and the need for further validation and optimization. Future research should focus on overcoming the remaining technical and computational barriers, refining the proposed methods, and conducting extensive validation studies to facilitate the clinical adoption of high-precision hyperthermia treatments. This thesis represents a significant step towards improving the precision and effectiveness of hyperthermia therapy, offering a comprehensive framework for future advancements in this field.