Li Ling-Rui, Du Bo, Liu Han-Qing, Chen Chuang
Department of Breast and Thyroid Surgery, Renmin Hospital of Wuhan University, Wuhan, China.
School of Computer Science, Wuhan University, Wuhan, China.
Front Oncol. 2021 Feb 9;10:604051. doi: 10.3389/fonc.2020.604051. eCollection 2020.
Thyroid cancers (TC) have increasingly been detected following advances in diagnostic methods. Risk stratification guided by refined information becomes a crucial step toward the goal of personalized medicine. The diagnosis of TC mainly relies on imaging analysis, but visual examination may not reveal much information and not enable comprehensive analysis. Artificial intelligence (AI) is a technology used to extract and quantify key image information by simulating complex human functions. This latent, precise information contributes to stratify TC on the distinct risk and drives tailored management to transit from the surface (population-based) to a point (individual-based). In this review, we started with several challenges regarding personalized care in TC, for example, inconsistent rating ability of ultrasound physicians, uncertainty in cytopathological diagnosis, difficulty in discriminating follicular neoplasms, and inaccurate prognostication. We then analyzed and summarized the advances of AI to extract and analyze morphological, textural, and molecular features to reveal the ground truth of TC. Consequently, their combination with AI technology will make individual medical strategies possible.
随着诊断方法的进步，甲状腺癌（TC）的检出率日益增加。基于精确信息的风险分层成为实现个性化医疗目标的关键一步。TC的诊断主要依靠影像分析，但肉眼检查可能无法揭示太多信息，也无法进行全面分析。人工智能（AI）是一种通过模拟复杂人类功能来提取和量化关键图像信息的技术。这种潜在的精确信息有助于根据不同风险对TC进行分层，并推动从表面（基于人群）到点（基于个体）的个性化管理。在本综述中，我们首先探讨了TC个性化治疗面临的几个挑战，例如超声医生评级能力不一致、细胞病理学诊断存在不确定性、鉴别滤泡性肿瘤困难以及预后预测不准确。然后，我们分析并总结了AI在提取和分析形态学、纹理和分子特征以揭示TC真实情况方面的进展。因此，将它们与AI技术相结合将使个性化医疗策略成为可能。
