CT radiomics prediction of CXCL9 expression and survival in ovarian cancer

作者：Rui Gu, Siyi Tan, Yuping Xu, Donghui Pan, Ce Wang, Min Zhao, Jiajun Wang, Liwei Wu, Shaojie Zhao, Feng Wang, Min Yang · 发表于：Journal of Ovarian Research · 年份：2023 · DOI：10.1186/s13048-023-01248-5 · 被引用次数：12 · 研究领域：Ferroptosis and cancer prognosis、Radiomics and Machine Learning in Medical Imaging、Cancer Immunotherapy and Biomarkers

BACKGROUND: C-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. METHODS: We 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. RESULTS: CXCL9 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 u...