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.
背景/目的：甲状腺髓样癌（MTC）是一种罕见但侵袭性强的甲状腺癌，尽管发病率较低，却在甲状腺癌相关死亡中占据不成比例的高比例。MTC因其异质性而与其他分化型甲状腺恶性肿瘤不同，在遗传性和散发性病例中均呈现复杂性。主要针对甲状腺乳头状癌（PTC）设计的传统管理指南，难以满足MTC患者所需的个体化诊疗需求。近年来，临床评估、放射影像、病理检测、基因突变和免疫谱分析产生的海量数据，已超出临床医生人工同步分析与整合这些多元数据流的能力范围。这种数据洪流迫切需要采用先进技术辅助决策过程。整合多种组学技术与人工智能（AI）的全息组学方法，成为应对这些挑战的有力解决方案。 方法：本文综述了AI驱动的精准肿瘤学如何通过快速准确处理大量复杂数据，优化MTC患者的诊断流程、分期、风险分层、治疗及随访管理。检索范围限定于PubMed收录的英文文献。 结果：AI算法能够识别临床医生难以察觉的模式与关联，从而提升个体化治疗方案的精准度。此外，AI在MTC管理中的应用有助于整合全球临床经验，深化对疾病及其治疗结局的系统认知。 结论：全息组学与AI在MTC患者管理中的融合，标志着精准肿瘤学领域的重大进展。这一创新方法不仅能够应对罕见侵袭性疾病的复杂性，还为全球协作与公平医疗解决方案开辟道路，最终改变MTC患者的治疗与护理格局。通过运用AI与全息组学技术，我们有望实现不分经济地位的个体化医疗普惠，从而提升全球MTC患者的总生存率与生活质量。这一全球性实践符合联合国可持续发展目标3——确保各年龄段人群的健康生活与福祉。
肿瘤(癌症)患者之家