CT-based machine learning radiomics predicts CCR5 expression level and survival in ovarian cancer

Journal of Ovarian Research

Research

Shengyi Gu¹  Sheng Wan¹  Ronghua Che¹  Jing Peng¹  Xiaolin Hua²  Jiejun Cheng³  Ying Li⁴  Tianfan Zhou⁵  Yuelin Wu⁵ 

[1] Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China;Department of Gynecology and Obstetrics, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China;Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China;Department of Obstetrics, Shanghai First Maternity and infant hospital, Shanghai Tongji University School of Medicine, 2699 West Gaoke Road, 201204, Shanghai, China;Department of Radiology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China;Department of Radiology, Shanghai First Maternity and infant hospital, Shanghai Tongji University School of Medicine, 2699 West Gaoke Road, 201204, Shanghai, China;Reproductive Medicine Center, Tongji Hospital Affiliated to Tongji University, Shanghai, China;Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, 200092, Shanghai, China;

关键词: CCR5;    Ovarian cancer;    Radiomics;    Machine learning;    Prognosis;    Survival;   

DOI  :  10.1186/s13048-022-01089-8

 received in 2022-09-16, accepted in 2022-12-23,  发布年份 2022

来源: Springer

【 摘 要 】

ObjectiveWe aimed to evaluate the prognostic value of C-C motif chemokine receptor type 5 (CCR5) expression level for patients with ovarian cancer and to establish a radiomics model that can predict CCR5 expression level using The Cancer Imaging Archive (TCIA) and The Cancer Genome Atlas (TCGA) database.MethodsA total of 343 cases of ovarian cancer from the TCGA were used for the gene-based prognostic analysis. Fifty seven cases had preoperative computed tomography (CT) images stored in TCIA with genomic data in TCGA were used for radiomics feature extraction and model construction. 89 cases with both TCGA and TCIA clinical data were used for radiomics model evaluation. After feature extraction, a radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. A prognostic scoring system incorporating radiomics signature based on CCR5 expression level and clinicopathologic risk factors was proposed for survival prediction.ResultsCCR5 was identified as a differentially expressed prognosis-related gene in tumor and normal sample, which were involved in the regulation of immune response and tumor invasion and metastasis. Four optimal radiomics features were selected to predict overall survival. The performance of the radiomics model for predicting the CCR5 expression level with 10-fold cross- validation achieved Area Under Curve (AUCs) of 0.770 and of 0.726, respectively, in the training and validation sets. A predictive nomogram was generated based on the total risk score of each patient, the AUCs of the time-dependent receiver operating characteristic (ROC) curve of the model was 0.8, 0.673 and 0.792 for 1-year, 3-year and 5-year, respectively. Along with clinical features, important imaging biomarkers could improve the overall survival accuracy of the prediction model.ConclusionThe expression levels of CCR5 can affect the prognosis of patients with ovarian cancer. CT-based radiomics could serve as a new tool for prognosis prediction.

【 授权许可】

CC BY   
© The Author(s) 2022

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