Preoperative MRI-based radiomics analysis of intra- and peritumoral regions for predicting CD3 expression in early cervical cancer.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No. 136 Jinzhou Road, Xiangyang, 441021, Hubei Province, People's Republic of China.
- Department of Pathology, Xiangyang Central Hospital, Affiliated Hospital Of Hubei University of Arts and Science, Xiangyang, Hubei, People's Republic of China.
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, People's Republic of China.
- Hubei Provincial Clinical Research Center for Cervical Lesions, Xiangyang, People's Republic of China.
- Institute of Gynecological and Obstetric Disease, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, People's Republic of China.
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, 430030, People's Republic of China.
- Department of Research Collaboration, R&D Center, Hangzhou Deepwise & League of PHD Technology Co., Ltd, No. 88 Longyuan Road, Cangqian Street, Yuhang District, Hangzhou City, Zhejiang Province, 311101, People's Republic of China.
- Department of Obstetrics and Gynaecology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, People's Republic of China. [email protected].
- Hubei Provincial Clinical Research Center for Cervical Lesions, Xiangyang, People's Republic of China. [email protected].
- Institute of Gynecological and Obstetric Disease, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, People's Republic of China. [email protected].
- Department of Radiology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, No. 136 Jinzhou Road, Xiangyang, 441021, Hubei Province, People's Republic of China. [email protected].
Abstract
The study investigates the correlation between CD3 T-cell expression levels and cervical cancer (CC) while developing a magnetic resonance (MR) imaging-based radiomics model for preoperative prediction of CD3 T-cell expression levels. Prognostic correlations between CD3D, CD3E, and CD3G gene expressions and various cancers were analyzed using the Cancer Genome Atlas (TCGA) database. Protein-protein interaction (PPI) analysis via the STRING database identified associations between these genes and T lymphocyte activity. Gene Set Enrichment Analysis (GSEA) revealed immune pathway enrichment by categorizing genes based on CD3D expression levels. Correlations between immune checkpoint molecules and CD3 complex genes were also assessed. The study retrospectively included 202 patients with pathologically confirmed early-stage CC who underwent preoperative MRI, divided into training and test groups. Radiomic features were extracted from the whole-lesion tumor region of interest (ROI<sub>tumor</sub>) and from peritumoral regions with 3 mm and 5 mm margins (ROI<sub>3mm</sub> and ROI<sub>5mm</sub>, respectively). Various machine learning algorithms, including Support Vector Machine (SVM), Logistic Regression, Random Forest, AdaBoost, and Decision Tree, were used to construct radiomics models based on different ROIs, and diagnostic performances were compared to identify the optimal approach. The best-performing algorithm was combined with intra- and peritumoral features and clinically relevant independent risk factors to develop a comprehensive predictive model. Analysis of the TCGA database demonstrated significant associations between CD3D, CD3E, and CD3G expressions and several cancers, including CC (p < 0.05). PPI analysis highlighted connections between these genes and T lymphocyte function, while GSEA indicated enrichment of immune-related pathways linked to CD3D. Immune checkpoint correlations showed positive associations with CD3 complex genes. Radiomics analysis selected 18 features from ROI<sub>tumor</sub> and ROI<sub>3mm</sub> across MRI sequences. The SVM algorithm achieved the highest predictive performance for CD3 T-cell expression status, with an area under the curve (AUC) of 0.93 in the training group and 0.92 in the test group. This MR-based radiomics model effectively predicts CD3 expression status in patients with early-stage CC, offering a non-invasive tool for preoperative assessment of CD3 expression, but its clinical utility needs further prospective validation.