Thyroid Cancer (TC) is one of the most prevalent endocrine malignancies, with early detection being critical for patient management. The motivation for integrating Machine Learning (ML) in thyroid cancer research stems from the limitations of conventional diagnostic and monitoring approaches, as ML offers transformative potential for reducing human errors and improving prediction outcomes for diagnostic accuracy, risk stratification, treatment options, recurrence prognosis, and patient quality of life. This scoping review maps existing literature on ML applications in TC, particularly those leveraging clinical data, Electronic Medical Records (EMRs), and synthesized findings. This study analyzed 1231 papers, evaluated 203 full-text articles, selected 21 articles, and detailed three themes: (1) malignancy prediction and nodule classification; (2) other metastases derived from TC prediction; and (3) recurrence and survival prediction. This work examined the case studies’ characteristics and objectives and identified key trends and challenges in ML-driven TC research. Finally, this scoping review addressed the limitations of related and highlighted directions to enhance the clinical potential of ML in this domain while emphasizing its capability to transform TC patient care into advanced precision medicine.
甲状腺癌是最常见的内分泌恶性肿瘤之一，早期检测对患者管理至关重要。将机器学习整合到甲状腺癌研究中的动机源于传统诊断与监测方法的局限性，因为机器学习在降低人为误差、提升诊断准确性、风险分层、治疗方案选择、复发预后及患者生活质量等方面的预测结果具有变革性潜力。本范围综述系统梳理了机器学习在甲状腺癌应用领域的现有文献，重点关注利用临床数据、电子病历及综合研究成果的相关研究。本研究共分析1231篇文献，评估203篇全文，最终筛选21篇文章，详细阐述三大主题：(1)恶性预测与结节分类；(2)甲状腺癌转移预测；(3)复发与生存预测。通过剖析案例研究特征与目标，本研究揭示了机器学习驱动甲状腺癌研究的关键趋势与挑战。最后，本综述探讨了相关研究的局限性，指出了提升机器学习在该领域临床潜力的发展方向，并强调其推动甲状腺癌诊疗向精准医学转型的能力。
肿瘤(癌症)患者之家