Background/Objective: Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer comprising a disproportionate share of thyroid cancer-related mortalities, despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, which are designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for patients with MTC. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, which is an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. Methods: This article reviews how AI-driven precision oncology can enhance the diagnostic workup, staging, risk stratification, management, and follow-up care of patients with MTC by processing vast amounts of complex data quickly and accurately. Articles published in English language and indexed in Pubmed were searched. Results: AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in the management of MTC enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. Conclusions: The integration of holomics and AI in the management of patients with MTC represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of treatment and care of patients with MTC. By leveraging AI and holomics, we can strive toward making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being at all ages.

Medullary thyroid carcinoma (MTC) is a rare yet aggressive form of thyroid cancer, comprising a disproportionate share of thyroid cancer-related mortalities despite its low prevalence. MTC differs from other differentiated thyroid malignancies due to its heterogeneous nature, presenting complexities in both hereditary and sporadic cases. Traditional management guidelines, designed primarily for papillary thyroid carcinoma (PTC), fall short in providing the individualized care required for MTC patients. In recent years, the sheer volume of data generated from clinical evaluations, radiological imaging, pathological assessments, genetic mutations, and immunological profiles has made it humanly impossible for clinicians to simultaneously analyze and integrate these diverse data streams effectively. This data deluge necessitates the adoption of advanced technologies to assist in decision-making processes. Holomics, an integrated approach that combines various omics technologies, along with artificial intelligence (AI), emerges as a powerful solution to address these challenges. AI-driven precision oncology can significantly enhance the diagnostic work-up, staging, risk stratification, management, and follow-up care of MTC patients by processing vast amounts of complex data quickly and accurately. AI algorithms can identify patterns and correlations that may not be apparent to human clinicians, thereby improving the precision of personalized treatment plans. Moreover, the implementation of AI in MTC management enables the collation and synthesis of clinical experiences from across the globe, facilitating a more comprehensive understanding of the disease and its treatment outcomes. This global approach aligns with the United Nations Sustainable Development Goal 3, which aims to ensure healthy lives and promote well-being for all at all ages. By leveraging AI and holomics, we can strive towards making personalized healthcare accessible to every individual, regardless of their economic status, thereby improving overall survival rates and quality of life for MTC patients worldwide. In conclusion, the integration of holomics and AI in the management of medullary thyroid carcinoma represents a significant advancement in precision oncology. This innovative approach not only addresses the complexities of a rare and aggressive disease but also paves the way for global collaboration and equitable healthcare solutions, ultimately transforming the landscape of MTC treatment and care.

Follicular thyroid carcinoma (FTC) accounts for approximately 17-20% of well-differentiated thyroid cancer (WDTC) worldwide. In South Africa (SA), FTC is more common than reported internationally, however, available SA studies included small patient numbers. This multi-institutional study comprehensively describes FTC patients who underwent surgery over a 5-year period, focusing on presentation, diagnosis, and management. A retrospective review of all patients with FTC operated at 13 academic hospitals throughout SA between January 2015 and December 2019 was conducted. The Thyroid Cancer Group of South Africa collectively entered data on the presentation, diagnosis, management, and short-term outcomes of 464 thyroid cancer patients into a REDCap database. Descriptive statistical analysis was performed. Of 464 cases captured in the database, 103 (22.1%) were FTCs. WDTC was reported in 87.9% (408/464) of operations, with FTC comprising 25.2% (103/408). Of the 103 FTC patients, 82.5% (n = 85) were female and 16.5% (n = 17) male. The mean age was 51.8 years (SD 17.3). In cases where staging was reported, more than half (53.9%) presented with T3 tumours. Distant metastases were found in 12.6% of patients. Thyroid lobectomy was the most performed procedure (57.4%), followed by total thyroidectomy (37.6%). SA presents a multifaceted picture of FTC, with a higher incidence than in developed countries but lower than some other African nations. Patients are frequently symptomatic, which may predict worse outcomes. Standardised reporting and a national thyroid registry could assist in treatment planning and consistency of care.