Development of a radiomic–clinical nomogram for prediction of survival in patients with serous ovarian cancer

Abstract

To develop and validate a radiomic-clinical nomogram to evaluate overall survival (OS) postoperatively in patients with serous ovarian cancer. Eighty serous ovarian cancer patients from The Cancer Imaging Archive (TCIA) database were used as the training set, and 39 eligible patients treated at Affiliated Huadu Hospital were used as the independent validation set. In total, 1,301 radiomics features were extracted from ovarian cancer lesions on venous-phase computed tomography (CT) images. Then, a radiomics signature was developed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm in the training set. Moreover, a radiomic-clinical nomogram was constructed incorporating the radiomics signature and clinical predictors based on a multivariable Cox regression analysis. The performance of the nomogram was evaluated. Consisting of three selected features, the radiomics signature showed good discrimination in the training and validation sets with C-indexes of 0.694 (95% confidence interval [CI]: 0.613-0.775) and 0.709 (95% CI: 0.517-0.901), respectively. The radiomic-clinical nomogram contained the radiomics signature and four clinical predictors, including age, tumour size, pathological staging, and tumour grade. The nomogram showed favourable discrimination in the training set (C-index [95% CI], 0.754 [0.678-0.830]), which was confirmed in the validation set (C-index [95% CI], 0.727 [0.569-0.885]). According to the model, all patients were classified into high-risk and low-risk groups. Kaplan-Meier curves showed that there was a significant distinction between the OS of the high-risk and low-risk patients. The proposed radiomic-clinical nomogram can increase the predictive accuracy of OS in patients with serous ovarian cancer after surgery, which may aid in clinical decision-making.