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Updated: Sep 17, 2025

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Computer-Aided Three-Dimensional Visualization in the Treatment of Locally Advanced Thyroid Cancer

Published on: June 9, 2023

Global Thyroid Cancer Patterns and Predictive Analytics: Integrating Machine Learning for Advanced Diagnostic

Yao Sun¹, Yongsheng Jia², Kuan Fu¹

¹Department of Radiation Oncology, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.

Journal of Cellular and Molecular Medicine

|July 2, 2025

Summary

Thyroid cancer research reveals elevated inflammatory mediators and identifies key myeloid cell communication networks. These findings offer potential for improved diagnostics and targeted therapies in thyroid cancer.

Keywords:

MIF signalling pathway cancer immunology machine learning model myeloid cells thyroid cancer

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Area of Science:

Oncology
Molecular Biology
Immunology

Background:

Global increase in thyroid cancer incidence, especially in women.
Existing gaps in understanding molecular drivers and diagnostic tools for thyroid cancer.

Purpose of the Study:

Investigate molecular signatures in thyroid cancer.
Validate diagnostic markers for early detection.
Facilitate targeted therapeutic development.

Main Methods:

Quantitative PCR and ELISA for gene/cytokine analysis.
Single-cell transcriptomics for cellular communication.
Statistical analysis of differential expression and cytokine alterations.

Main Results:

Elevated pro-inflammatory (TNF-α, IL-6, IL-8, VEGF) and immunoregulatory (TGF-β, IL-10) cytokines (2.5-4.0 fold increase).
Distinct gene modules (MEblue, MEmagenta) correlated with disease progression.
Computational algorithms achieved 0.963 AUC for diagnosis; myeloid cell networks (MIF, GALECTIN) identified as key mediators.

Conclusions:

Expanded molecular understanding of thyroid carcinogenesis via myeloid-centered networks.
Identified molecular signatures and gene modules as potential diagnostic and therapeutic targets.
Need for prospective validation in diverse patient populations for clinical utility.