A CT-based radiomics nomogram for predicting early recurrence in patients with high-grade serous ovarian cancer. Issue 145 (December 2021)

• Record Type: Journal Article
• Authors: Chen, Hui-zhu; Wang, Xin-rong; Zhao, Fu-min; Chen, Xi-jian; Li, Xue-sheng; Ning, Gang; Guo, Ying-kun
• Is Part Of: European journal of radiology. Issue 145(2021)
• Publication Date: 2021-12
• Subjects: High-Grade Serous Ovarian Cancer -- Computed tomography -- Radiomics -- Recurrence; EOC epithelial ovarian cancer -- HGSOC high-grade serous ovarian cancer -- SVM-RFE recursive feature elimination based on support vector machine -- AUC area under the curve. ROC, Receiver operating characteristic curves -- Radscore radiomics signature -- CA125 carbohydrate antigen 125; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.110018
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: High-grade serous ovarian cancer has a high recurrence rate and poor outcome. Radiomics nomogram performed well for early recurrence prediction. Radiomics nomogram can differentiate high-risk patients from low-risk patients. Abstract: Purpose: To develop and validate a radiomics nomogram for predicting early recurrence in high-grade serous ovarian cancer (HGSOC) patients. Materials and Methods: From May 2008 to December 2019, 256 eligible HGSOC patients were enrolled and divided into training (n = 179) and test cohorts (n = 77) in a 7:3 ratio. A radiomics signature (Radscore) was selected by using recursive feature elimination based on a support vector machine (SVM-RFE) and building a radiomics model for recurrence prediction. Independent clinical risk factors were generated by univariable and multivariable Cox regression analyses. A combined model was developed based on the Radscore and independent clinical risk factors and presented as a radiomics nomogram. Its performance was assessed by AUC, Kaplan-Meier survival analysis and decision curve analysis. Results: Seven radiomics features were selected. The radiomics model yielded AUCs of 0.715 (95% CI: 0.640, 0.790) and 0.717 (95% CI: 0.600, 0.834) in the training and test cohorts, respectively. The clinical model (FIGO stage and residual disease) yielded AUCs of 0.632 and 0.691 in the training and test cohorts, respectively. The combined model demonstrated AUCs of 0.749 (95% CI: 0.678, 0.821) and 0.769 (95% CI: 0.662, 0.877) in the training and test cohorts, respectively. In the combined model, PFS was significantly shorter in the high-risk group than in the low-risk group (P < 0.0001). Conclusions: The radiomics nomogram performed well for early individualized recurrence prediction in patients with HGSOC and can also be used to differentiate high-risk patients from low-risk patients.