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<p>Thyroid cancer (TC) is the most common endocrine malignancy, with a steadily increasing global incidence that highlights the urgent need for advanced diagnostic and therapeutic strategies. This study examines peer-reviewed articles published between 2013 and 2025, focusing on environmental pollutants (Pesticides, PCBs, Phthalates, BPA, heavy metals, and Radiation) to analyze and screen for environmental risks and TC. This integrative review outlines the current state of TC, beginning with a detailed explanation of challenges in ensuring unbiassed TC diagnosis and treatment, including environmental pollutants and radiation exposure. It further explores multimodal management approaches for differentiated thyroid carcinoma (DTC) and addresses the significant challenges in ensuring equitable access to diagnosis and treatment. A key contribution of this work is a novel investigation into the role of deep learning models for enhancing diagnostic accuracy. In addition, an integrative review approach was utilized to combine multidisciplinary evidence, supported by an empirical experimental analysis. We have implemented the state-of-the-art models namely ResNet-50, DenseNet-201, and Vision Transformer (ViT) on a publicly available and private thyroid datasets. A principal innovation of our experimental study was the extension of these datasets to a more granular, six-class classification framework. The review concludes by combining these findings to propose future research directions, emphasizing the integration of artificial intelligence into clinical workflows to cover the way for more personalized, equitable, and effective management of thyroid cancer.</p>

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Environmental Risk Factors and Thyroid Cancer: An Integrative Review and Future Directions

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月28日

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  • Muneer Nusir,
  • Sajid Ullah Khan

摘要

Thyroid cancer (TC) is the most common endocrine malignancy, with a steadily increasing global incidence that highlights the urgent need for advanced diagnostic and therapeutic strategies. This study examines peer-reviewed articles published between 2013 and 2025, focusing on environmental pollutants (Pesticides, PCBs, Phthalates, BPA, heavy metals, and Radiation) to analyze and screen for environmental risks and TC. This integrative review outlines the current state of TC, beginning with a detailed explanation of challenges in ensuring unbiassed TC diagnosis and treatment, including environmental pollutants and radiation exposure. It further explores multimodal management approaches for differentiated thyroid carcinoma (DTC) and addresses the significant challenges in ensuring equitable access to diagnosis and treatment. A key contribution of this work is a novel investigation into the role of deep learning models for enhancing diagnostic accuracy. In addition, an integrative review approach was utilized to combine multidisciplinary evidence, supported by an empirical experimental analysis. We have implemented the state-of-the-art models namely ResNet-50, DenseNet-201, and Vision Transformer (ViT) on a publicly available and private thyroid datasets. A principal innovation of our experimental study was the extension of these datasets to a more granular, six-class classification framework. The review concludes by combining these findings to propose future research directions, emphasizing the integration of artificial intelligence into clinical workflows to cover the way for more personalized, equitable, and effective management of thyroid cancer.