From Data to Insights: A Comprehensive Survey on Advanced Applications
  in Thyroid Cancer Research
Xinyu Zhang Vincent CS Lee Feng Liu
Thyroid cancer, the most prevalent endocrine cancer, has gained significant
global attention due to its impact on public health. Extensive research efforts
have been dedicated to leveraging artificial intelligence (AI) methods for the
early detection of this disease, aiming to reduce its morbidity rates. However,
a comprehensive understanding of the structured organization of research
applications in this particular field remains elusive. To address this
knowledge gap, we conducted a systematic review and developed a comprehensive
taxonomy of machine learning-based applications in thyroid cancer pathogenesis,
diagnosis, and prognosis. Our primary objective was to facilitate the research
community's ability to stay abreast of technological advancements and
potentially lead the emerging trends in this field. This survey presents a
coherent literature review framework for interpreting the advanced techniques
used in thyroid cancer research. A total of 758 related studies were identified
and scrutinized. To the best of our knowledge, this is the first review that
provides an in-depth analysis of the various aspects of AI applications
employed in the context of thyroid cancer. Furthermore, we highlight key
challenges encountered in this domain and propose future research opportunities
for those interested in studying the latest trends or exploring
less-investigated aspects of thyroid cancer research. By presenting this
comprehensive review and taxonomy, we contribute to the existing knowledge in
the field, while providing valuable insights for researchers, clinicians, and
stakeholders in advancing the understanding and management of this disease.