Mathematical models applied to thyroid cancer

Area of Science:

• Endocrinology
• Mathematical Oncology
• Biophysics

Background:

• Thyroid cancer is the most common endocrine neoplasia globally.
• Mathematical modeling, particularly with differential equations, is increasingly used in cancer research.
• A comprehensive review of these models in thyroid cancer studies is needed.

Purpose of the Study:

• To review the application of mathematical models with differential equations in thyroid cancer research since 2006.
• To identify common modeling approaches and variables used in thyroid cancer studies.
• To understand the role of mathematical models in the detection, evolution, and treatment of thyroid cancer.

Main Methods:

• Systematic literature review of studies from 2006 to the present.
• Focus on research utilizing ordinary or partial differential equations for thyroid cancer modeling.
• Analysis of model applications, variables, and outcomes.

Main Results:

• Ordinary and partial differential equations are the most frequent mathematical tools employed.
• Models address tumor growth, radioiodine efficacy post-thyroidectomy, iodine-131 treatment, and pre-operative thyroid volume.
• Common variables include tumor/thyroid volume, iodine levels, thyroglobulin, thyroxine, radioiodine activity, and physical properties.

Conclusions:

• Mathematical models with differential equations offer valuable insights into various aspects of thyroid cancer.
• These models contribute to treatment execution and follow-up strategies.
• Further development of novel models is essential for advancing the understanding and management of thyroid cancer.