Lixandru-Petre Irina-Oana, Dima Alexandru, Musat Madalina, Dascalu Mihai, Gradisteanu Pircalabioru Gratiela, Iliescu Florina Silvia, Iliescu Ciprian
eBio-Hub Centre of Excellence in Bioengineering, National University of Science and Technology POLITEHNICA Bucharest, 060042 Bucharest, Romania.
Academy of Romanian Scientists, Ilfov 3, 050044 Bucharest, Romania.
Cancers (Basel). 2025 Apr 12;17(8):1308. doi: 10.3390/cancers17081308.
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（TC）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（ML）整合到甲状腺癌研究中的动机源于传统诊断和监测方法的局限性，因为ML具有减少人为错误和改善诊断准确性、风险分层、治疗选择、复发预后及患者生活质量预测结果的变革潜力。本综述梳理了关于ML在TC中的应用的现有文献，特别是那些利用临床数据、电子病历（EMR）和综合研究结果的文献。本研究分析了1231篇论文，评估了203篇全文文章，选取了21篇文章，并详细阐述了三个主题：（1）恶性预测和结节分类；（2）TC衍生的其他转移预测；（3）复发和生存预测。这项工作研究了案例研究的特征和目标，并确定了ML驱动的TC研究中的关键趋势和挑战。最后，本综述探讨了相关研究的局限性，并强调了提高ML在该领域临床潜力的方向，同时强调其将TC患者护理转变为先进精准医学的能力。
