Scholarlink

<p>The subtle imaging features of thymic epithelial tumors (TETs), which comprise multiple pathological subtypes of thymoma and thymic carcinoma, are of great significance for the identification of high-risk patients. Finding the radiomics features related to the immunohistochemical markers of TETs may provide a non-invasive method for the construction of a prediction model. This retrospective study analyzed non-enhanced computed tomography (NECT) images of 307 patients with TETs from two institutions. The radiomic features were extracted, clustered, and used to develop the models with machine learning algorithms. In general, the radiomics of TET patients were profiled and clustered into three clusters, which showed differences in correlation between clinicopathological characteristics, including histological type, Masaoka stage, and immunohistochemical results. Moreover, the “original-shape-flatness” and “wavelet-LHL-first-order-Median” were the most strongly correlated with CD117 and TDT expression, and the combined model of the two demonstrated predictive efficacy for CD117/TDT expression and risk groups in training and validation cohorts. This study highlights that radiomics and biomarker-associated features can serve as a non-invasive predictive biomarker for TET patients.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月24日

查看全文
  • Yutian Zhang,
  • Yangzhong Guo,
  • Junyu Li,
  • Haitao Jiang,
  • Yueyu Huang,
  • Bojian Feng,
  • Wenhui Shen,
  • You Xiao,
  • Jiahui Wang,
  • Chang Yu,
  • Changchun Wang,
  • Qiaoli Lv,
  • An Zhao,
  • Weimin Mao

摘要

The subtle imaging features of thymic epithelial tumors (TETs), which comprise multiple pathological subtypes of thymoma and thymic carcinoma, are of great significance for the identification of high-risk patients. Finding the radiomics features related to the immunohistochemical markers of TETs may provide a non-invasive method for the construction of a prediction model. This retrospective study analyzed non-enhanced computed tomography (NECT) images of 307 patients with TETs from two institutions. The radiomic features were extracted, clustered, and used to develop the models with machine learning algorithms. In general, the radiomics of TET patients were profiled and clustered into three clusters, which showed differences in correlation between clinicopathological characteristics, including histological type, Masaoka stage, and immunohistochemical results. Moreover, the “original-shape-flatness” and “wavelet-LHL-first-order-Median” were the most strongly correlated with CD117 and TDT expression, and the combined model of the two demonstrated predictive efficacy for CD117/TDT expression and risk groups in training and validation cohorts. This study highlights that radiomics and biomarker-associated features can serve as a non-invasive predictive biomarker for TET patients.