Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches

• Published in: Life sciences (1973) 2023-11, Vol.332, p.122110-122110, Article 122110
• Main Authors: Bhattacharya, Srinjan, Mahato, Rahul Kumar, Singh, Satwinder, Bhatti, Gurjit Kaur, Mastana, Sarabjit Singh, Bhatti, Jasvinder Singh
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Epigenetic modifications; Genetic modulators; lncRNA; miRNA; Personalized medicine; Thyroid carcinoma
• ISSN: 0024-3205; 1879-0631
• DOI: 10.1016/j.lfs.2023.122110

Thyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future. Leveraging AI, Machine Learning, and Deep Learning in Thyroid Cancer Diagnosis. This involves the extraction of features such as texture from primary diagnostic imaging like ultrasonography, along with tissue histology data and image patches. These inputs train machine learning and deep learning algorithms, enhancing their ability to accurately predict cancer detection outcomes. [Display omitted]