Chung Hyun Jung, Han Kyunghwa, Lee Eunjung, Yoon Jung Hyun, Park Vivian Youngjean, Lee Mina, Cho Eun, Kwak Jin Young
J Korean Soc Radiol. 2023 Jan;84(1):185-196. doi: 10.3348/jksr.2021.0155. Epub 2023 Jan 30.
This study aimed to investigate radiomics analysis of ultrasonographic images to develop a potential biomarker for predicting lymph node metastasis in papillary thyroid carcinoma (PTC) patients.
This study included 431 PTC patients from August 2013 to May 2014 and classified them into the training and validation sets. A total of 730 radiomics features, including texture matrices of gray-level co-occurrence matrix and gray-level run-length matrix and single-level discrete two-dimensional wavelet transform and other functions, were obtained. The least absolute shrinkage and selection operator method was used for selecting the most predictive features in the training data set.
Lymph node metastasis was associated with the radiomics score ( < 0.001). It was also associated with other clinical variables such as young age ( = 0.007) and large tumor size ( = 0.007). The area under the receiver operating characteristic curve was 0.687 (95% confidence interval: 0.616-0.759) for the training set and 0.650 (95% confidence interval: 0.575-0.726) for the validation set.
This study showed the potential of ultrasonography-based radiomics to predict cervical lymph node metastasis in patients with PTC; thus, ultrasonography-based radiomics can act as a biomarker for PTC.
本研究旨在探讨超声图像的放射组学分析，以开发一种潜在的生物标志物来预测甲状腺乳头状癌（PTC）患者的淋巴结转移。
本研究纳入了2013年8月至2014年5月期间的431例PTC患者，并将他们分为训练集和验证集。共获得了730个放射组学特征，包括灰度共生矩阵和灰度游程长度矩阵的纹理矩阵以及单级离散二维小波变换等功能。采用最小绝对收缩和选择算子方法在训练数据集中选择最具预测性的特征。
淋巴结转移与放射组学评分相关（<0.001）。它还与其他临床变量相关，如年轻（=0.007）和肿瘤体积大（=0.007）。训练集的受试者工作特征曲线下面积为0.687（95%置信区间：0.616-0.759），验证集为0.650（95%置信区间：0.575-0.726）。
本研究显示了基于超声的放射组学在预测PTC患者颈部淋巴结转移方面的潜力；因此，基于超声的放射组学可作为PTC的生物标志物。
