参与各种人类癌症病理过程的C-X-C模体趋化因子配体9（CXCL9）近年来成为研究热点。我们开发了一个放射组学模型来识别卵巢癌（OC）中的CXCL9状态，并评估其预后意义。我们使用TCIA和TCGA数据库分析OC中CXCL9的增强CT扫描、转录组测序数据和相应的临床特征。我们使用重复最小绝对收缩（LASSO）和递归特征消除（RFE）方法在提取和归一化后确定放射组学特征。我们基于逻辑回归和内部十倍交叉验证构建了用于CXCL9预测的放射组学模型。最后，基于Cox回归建立了60个月总体生存（OS）树状图，以分析存活数据。CXCL9 mRNA水平和其他几个涉及T细胞浸润的基因与OC患者的OS显著相关。我们的放射组学模型的放射组学分数（rad_score）基于五个特征来计算CXCL9的预测。训练队列的受试者特征曲线下面积（AUC-ROC）为0.781，而验证队列的AUC-ROC为0.743。高rad_score的患者有更好的总体生存（P < 0.001）。此外，校准曲线和决策曲线分析（DCA）显示预测和实际观察之间有良好的一致性，证明了我们模型的临床实用性。在OC患者中，CT扫描的放射组学标志（RS）可以区分CXCL9表达水平并预测预后，可能实现精准医学的最终目标。© 2023年BioMed Central Ltd., Springer Nature的一部分。

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.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.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 utility of our model.In 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.© 2023. BioMed Central Ltd., part of Springer Nature.