Immunohistochemical biomarker-associated radiomics for classifying thymic epithelial tumors: a multicenter retrospective study.
Authors
Affiliations (12)
Affiliations (12)
- Zhejiang Cancer Institute, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China.
- Thoracic Oncology Laboratory, Jiangxi Cancer Hospital, Nanchang Medical College, Nanchang, Jiangxi, China.
- Department of Radiology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Ultrasound Imaging Department, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Laboratory for Molecular and Cellular Therapy (LMCT), Vrije Universiteit Brussel, Brussels, Belgium.
- Department of Pathology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
- Zhejiang Cancer Institute, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
- Thoracic Oncology Laboratory, Jiangxi Cancer Hospital, Nanchang Medical College, Nanchang, Jiangxi, China. [email protected].
- Postgraduate training base Alliance of Wenzhou Medical University (Zhejiang Cancer Hospital), Hangzhou, Zhejiang, China. [email protected].
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China. [email protected].
Abstract
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.