Breast Cancer Estimate Modeling via PDE Thermal Analysis Algorithms

Area of Science:

• Biomedical Engineering
• Medical Physics
• Computational Biology

Background:

• Nondestructive testing (NDT) is crucial for defect detection but often lacks precision and can harm the body.
• Current NDT methods for breast cancer prediction have limitations in accuracy and safety.
• Reliable and non-invasive diagnostic tools are essential for early breast cancer detection.

Purpose of the Study:

• To develop and validate a theoretical model for nondestructive breast cancer detection using thermal analysis.
• To enhance the reliability and accuracy of breast cancer prediction methods.
• To explore the potential of thermal imaging in medical diagnosis beyond breast cancer.

Main Methods:

• Construction of a theoretical model simulating inner breast tissue.
• Application of two-dimensional modeling and partial differential equation (PDE) thermal analysis.
• Estimation of simulated breast cancer presence and extent based on thermal observations.

Main Results:

• Demonstrated the feasibility of non-wounding defect detection through theoretical analysis.
• Validated the use of thermal analysis for estimating simulated breast cancer characteristics.
• Established the potential for long-term breast cancer prediction using thermal data.

Conclusions:

• Nondestructive thermal analysis offers a reliable and preferable alternative for defect detection and medical diagnosis.
• The developed model shows promise for accurate breast cancer prediction and can be extended to other cancer types.
• This research advances the field of non-invasive diagnostic techniques in oncology.