Cao Yuan, Zhong Xiao, Diao Wei, Mu Jingshi, Cheng Yue, Jia Zhiyun
Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu 610040, China.
Department of Radiology, West China Hospital of Sichuan University, Chengdu 610040, China.
Cancers (Basel). 2021 May 18;13(10):2436. doi: 10.3390/cancers13102436.
Radiomics is an emerging technique that allows the quantitative extraction of high-throughput features from single or multiple medical images, which cannot be observed directly with the naked eye, and then applies to machine learning approaches to construct classification or prediction models. This method makes it possible to evaluate tumor status and to differentiate malignant from benign tumors or nodules in a more objective manner. To date, the classification and prediction value of radiomics in DTC patients have been inconsistent. Herein, we summarize the available literature on the classification and prediction performance of radiomics-based DTC in various imaging techniques. More specifically, we reviewed the recent literature to discuss the capacity of radiomics to predict lymph node (LN) metastasis, distant metastasis, tumor extrathyroidal extension, disease-free survival, and B-Raf proto-oncogene serine/threonine kinase (BRAF) mutation and differentiate malignant from benign nodules. This review discusses the application and limitations of the radiomics process, and explores its ability to improve clinical decision-making with the hope of emphasizing its utility for DTC patients.
放射组学是一种新兴技术，它能够从单张或多张医学图像中定量提取肉眼无法直接观察到的高通量特征，然后应用于机器学习方法来构建分类或预测模型。该方法使得以更客观的方式评估肿瘤状态以及区分恶性肿瘤与良性肿瘤或结节成为可能。迄今为止，放射组学在分化型甲状腺癌（DTC）患者中的分类和预测价值一直存在不一致的情况。在此，我们总结了关于基于放射组学的DTC在各种成像技术中的分类和预测性能的现有文献。更具体地说，我们回顾了近期文献，以探讨放射组学预测淋巴结（LN）转移、远处转移、肿瘤甲状腺外侵犯、无病生存期以及B-Raf原癌基因丝氨酸/苏氨酸激酶（BRAF）突变的能力，以及区分恶性与良性结节的能力。本综述讨论了放射组学过程的应用和局限性，并探讨了其改善临床决策的能力，希望强调其对DTC患者的实用性。
