Luvhengo Thifhelimbilu Emmanuel, Moeng Maeyane Stephens, Sishuba Nosisa Thabile, Makgoka Malose, Jonas Lusanda, Mamathuntsha Tshilidzi Godfrey, Mbambo Thandanani, Kagodora Shingirai Brenda, Dlamini Zodwa
Department of Surgery, University of the Witwatersrand, Johannesburg 2193, South Africa.
Department of Surgery, University of Pretoria, Pretoria 0002, South Africa.
Cancers (Basel). 2024 Oct 13;16(20):3469. doi: 10.3390/cancers16203469.
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. 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. 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. 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.
甲状腺髓样癌(MTC)是一种罕见但侵袭性强的甲状腺癌,尽管其发病率较低,但在甲状腺癌相关死亡中所占比例却过高。MTC因其异质性,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出复杂性。传统的管理指南主要是为乳头状甲状腺癌(PTC)设计的,在为MTC患者提供所需的个性化护理方面存在不足。近年来,临床评估、放射影像学、病理评估、基因突变和免疫谱分析产生的数据量巨大,使得临床医生根本无法同时有效地分析和整合这些不同的数据流。这种数据洪流需要采用先进技术来辅助决策过程。整合组学是一种结合各种组学技术以及人工智能(AI)的综合方法,是应对这些挑战的有力解决方案。本文回顾了人工智能驱动的精准肿瘤学如何通过快速准确地处理大量复杂数据,来加强MTC患者的诊断检查、分期、风险分层、管理和随访护理。检索了发表在英文且被PubMed索引的文章。人工智能算法可以识别临床医生可能不明显的模式和相关性,从而提高个性化治疗方案的精准度。此外,在MTC管理中实施人工智能能够整理和综合全球各地的临床经验,有助于更全面地了解该疾病及其治疗结果。整合组学和人工智能在MTC患者管理中的应用代表了精准肿瘤学的重大进展。这种创新方法不仅解决了一种罕见且侵袭性疾病的复杂性,还为全球合作和公平的医疗保健解决方案铺平了道路,最终改变了MTC患者的治疗和护理格局。通过利用人工智能和整合组学,我们可以努力使每个人都能获得个性化医疗保健,无论其经济状况如何,从而提高全球MTC患者的总体生存率和生活质量。这种全球方法符合联合国可持续发展目标3,该目标旨在确保健康生活并促进各年龄段的福祉。