ArticleJournal of ovarian research2023
CT radiomics prediction of CXCL9 expression and survival in ovarian cancer.
Article in Journal of ovarian research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- CCL8 suppresses ovarian cancer progression via M1 macrophage polarization and NF-κB-mediated apoptosis.Scientific reports · 2025Article
- Multi-omics study of prognostic models and molecular networks related to ovarian cancer.Discover oncology · 2025Article
- Multimodal fusion radiomic-immunologic scoring model: accurate identification of prostate cancer progression.BMC medical imaging · 2025Article
- Single-cell RNA sequencing reveals the change in cytotoxic NK/T cells, epithelial cells and myeloid cells of the tumor microenvironment of high-grade serous ovarian carcinoma.Discover oncology · 2024Article
- Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model.Heliyon · 2024Article
- Single-cell analysis of matrisome-related genes in breast invasive carcinoma: new avenues for molecular subtyping and risk estimation.Frontiers in immunology · 2024Article
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundC-X-C motif chemokine ligand 9 (CXCL9), which is involved in the pathological processes of various human cancers, has become a hot topic in recent years. We developed a radiomic model to identify CXCL9 status in ovarian cancer (OC) and evaluated its prognostic significance.
methodsWe analyzed enhanced CT scans, transcriptome sequencing data, and corresponding clinical characteristics of CXCL9 in OC using the TCIA and TCGA databases. We used the repeat least absolute shrinkage (LASSO) and recursive feature elimination(RFE) methods to determine radiomic features after extraction and normalization. We constructed a radiomic model for CXCL9 prediction based on logistic regression and internal tenfold cross-validation. Finally, a 60-month overall survival (OS) nomogram was established to analyze survival data based on Cox regression.
resultsCXCL9 mRNA levels and several other genes involving in T-cell infiltration were significantly relevant to OS in OC patients. The radiomic score (rad_score) of our radiomic model was calculated based on the five features for CXCL9 prediction. The areas under receiver operating characteristic (ROC) curves (AUC-ROC) for the training cohort was 0.781, while that for the validation cohort was 0.743. Patients with a high rad_score had better overall survival (P < 0.001). In addition, calibration curves and decision curve analysis (DCA) showed good consistency between the prediction and actual observations, demonstrating the clinical utility of our model.
conclusionIn patients with OC, the radiomics signature(RS) of CT scans can distinguish the level of CXCL9 expression and predict prognosis, potentially fulfilling the ultimate purpose of precision medicine.
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