Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Department of Ultrasound, China-Japan Friendship Hospital, Beijing, China.
Front Endocrinol (Lausanne). 2022 Aug 19;13:915135. doi: 10.3389/fendo.2022.915135. eCollection 2022.
The preoperative identification of mutation could assist to make appropriate treatment strategies for patients with papillary thyroid microcarcinoma (PTMC). This study aimed to establish an ultrasound (US) radiomics nomogram for the assessment of status.
A total of 328 PTMC patients at the China-Japan Friendship Hospital between February 2019 and November 2021 were enrolled in this study. They were randomly divided into training ( = 232) and validation ( = 96) cohorts. Radiomics features were extracted from the US images. The least absolute shrinkage and selection operator (LASSO) regression was applied to select the status-related features and calculate the radiomics score (Rad-score). Univariate and multivariate logistic regression analyses were subsequently performed to identify the independent factors among Rad-score and conventional US features. The US radiomics nomogram was established and its predictive performance was evaluated discrimination, calibration, and clinical usefulness in the training and validation sets.
Multivariate analysis indicated that the Rad-score, composition, and aspect ratio were independent predictive factors of status. The US radiomics nomogram which incorporated the three variables showed good calibration. The discrimination of the US radiomics nomogram showed better discriminative ability than the conventional US model both in the training set (AUC 0.685 vs. 0.592) and validation set (AUC 0.651 vs. 0.622). Decision curve analysis indicated the superior clinical applicability of the nomogram compared to the conventional US model.
The US radiomics nomogram displayed better performance than the conventional US model in predicting mutation in patients with PTMC.
术前识别 突变有助于为甲状腺微小乳头状癌（PTMC）患者制定适当的治疗策略。本研究旨在建立一种超声（US）放射组学列线图来评估 状态。
本研究共纳入 2019 年 2 月至 2021 年 11 月期间在中国医学科学院北京协和医院就诊的 328 例 PTMC 患者。将患者随机分为训练集（n=232）和验证集（n=96）。从 US 图像中提取放射组学特征。采用最小绝对收缩和选择算子（LASSO）回归筛选与 状态相关的特征并计算放射组学评分（Rad-score）。随后进行单因素和多因素逻辑回归分析，以确定 Rad-score 与常规 US 特征之间的独立因素。建立 US 放射组学列线图，并在训练集和验证集中评估其预测性能（区分度、校准度和临床实用性）。
多因素分析表明，Rad-score、成分和纵横比是 状态的独立预测因素。该列线图整合了这三个变量，校准度良好。在训练集（AUC 0.685 比 0.592）和验证集（AUC 0.651 比 0.622）中，US 放射组学列线图的区分度均显示出比常规 US 模型更好的区分能力。决策曲线分析表明，与常规 US 模型相比，该列线图具有更好的临床适用性。
与常规 US 模型相比，US 放射组学列线图在预测 PTMC 患者 突变方面具有更好的性能。
