Journal of Ovarian Research | |
CT radiomics prediction of CXCL9 expression and survival in ovarian cancer | |
Research | |
Shaojie Zhao1  Liwei Wu1  Min Zhao1  Jiajun Wang1  Feng Wang2  Yuping Xu3  Ce Wang3  Donghui Pan3  Siyi Tan4  Min Yang4  Rui Gu5  | |
[1] Department of Gynecology, Wuxi Maternity and Child Health Care Hospital, Wuxi School of Medicine, Jiangnan University, 214002, Wuxi, China;Department of Gynecology, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, 214000, Wuxi, China;Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, 210001, Nanjing, China;Key Laboratory of Nuclear Medicine, Ministry of Health, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 214063, Wuxi, China;School of Pharmacy, Nanjing Medical University, 211166, Nanjing, China;Key Laboratory of Nuclear Medicine, Ministry of Health, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 214063, Wuxi, China;School of Pharmacy, Nanjing Medical University, 211166, Nanjing, China;Key Laboratory of Nuclear Medicine, Ministry of Health, Jiangsu Key Laboratory of Molecular Nuclear Medicine, Jiangsu Institute of Nuclear Medicine, 214063, Wuxi, China;Department of Gynecology, Wuxi Maternity and Child Health Care Hospital, Wuxi School of Medicine, Jiangnan University, 214002, Wuxi, China;Department of Gynecology, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, 214000, Wuxi, China; | |
关键词: Ovarian cancer; CXCL9; Radiomics; Prognosis; Overall survival; | |
DOI : 10.1186/s13048-023-01248-5 | |
received in 2023-05-07, accepted in 2023-07-27, 发布年份 2023 | |
来源: Springer | |
【 摘 要 】
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.
【 授权许可】
CC BY
© BioMed Central Ltd., part of Springer Nature 2023
【 预 览 】
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