甲状腺癌诊断中的放射组学与人工智能：概念、挑战与解决方案
Radiomics and artificial Intelligence for thyroid cancer diagnosis: Concepts, challenges, and solutions
AI总结：
本综述基于PRISMA系统检索纳入42项研究，总结AI与放射组学结合超声图像在甲状腺癌诊断中的有效性，并指出可解释性、数据限制等挑战及标准化、多中心研究等解决方向。
摘要：
甲状腺癌是一个日益严重的全球健康问题，需要先进的诊断方法。本综述探讨了人工智能和放射组学在甲状腺癌诊断中的应用。按照PRISMA指南，截至2024年10月对多个数据库进行了检索。通过关键词组合，发现了一篇关于甲状腺癌及相关主题的英文学术出版物。在去除112篇重复文献后，原始检索返回了368篇论文。在根据摘要和标题检查排除了176篇文章后，按照预定标准筛选出相关研究。经过全面分析，又排除了6项研究。在纳入的42项研究中，结合超声（US）图像的放射组学分析在诊断甲状腺癌方面显示出有效性。研究结果各异，部分研究提出了优于现状的新策略。文献强调了AI模型面临的各种挑战，包括可解释性问题、数据集限制和操作者依赖性。42项纳入研究的综合结果提到需要标准化工作和前瞻性多中心研究来解决这些问题。此外，还确定了克服这些障碍的方法，如可解释AI技术的进步和个性化医疗技术。本综述重点关注AI和放射组学如何改变甲状腺癌的诊断和治疗。尽管存在挑战，但未来在多学科合作、临床适用性验证和算法改进方面的研究有望改善甲状腺癌治疗中的患者预后和诊断精度。
英文摘要：
Thyroid cancer is an increasing global health concern that requires advanced diagnostic methods. The application of AI and radiomics to thyroid cancer diagnosis is examined in this review. A review of multiple databases was conducted in compliance with PRISMA guidelines until October 2024. A combination of keywords led to the discovery of an English academic publication on thyroid cancer and related subjects. 368 papers were returned from the original search after 112 duplicates were removed. Relevant studies were selected according to predetermined criteria after 176 articles were eliminated based on an examination of their abstract and title. After the comprehensive analysis, an additional six studies were excluded. Among the 42 included studies, radiomics analysis, which incorporates ultrasound (US) images, demonstrated its effectiveness in diagnosing thyroid cancer. Various results were noted, some of the studies presenting new strategies that outperformed the status quo. The literature has emphasized various challenges faced by AI models, including interpretability issues, dataset constraints, and operator dependence. The synthesized findings of the 42 included studies mentioned the need for standardization efforts and prospective multicenter studies to address these concerns. Furthermore, approaches to overcome these obstacles were identified, such as advances in explainable AI technology and personalized medicine techniques. The review focuses on how AI and radiomics could transform the diagnosis and treatment of thyroid cancer. Despite challenges, future research on multidisciplinary cooperation, clinical applicability validation, and algorithm improvement holds the potential to improve patient outcomes and diagnostic precision in the treatment of thyroid cancer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
