A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study

Abstract

Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC). Methods: This study had a retrospective multicenter (two hospitals in China) design and a radiomic analysis was performed using contrast enhanced CT in advanced HGSOC (FIGO stage III or IV) patients. We used a minimum 18-month follow-up period for all patients (median 38.8 months, range 18.8-81.8 months). All patients were divided into three cohorts according to the timing of their surgery and hospital stay: training cohort (TC) and internal validation cohort (IVC) were from one hospital, and independent external validation cohort (IEVC) was from another hospital. A total of 620 3-D radiomic features were extracted and a Lasso-Cox regression was used for feature dimension reduction and determination of radiomic signature. Finally, we combined the radiomic signature with seven common clinical variables to develop a novel nomogram using a multivariable Cox proportional hazards model. Results: A final 142 advanced HGSOC patients were enrolled. Patients were successfully divided into two groups with statistically significant differences based on radiomic signature, consisting of four radiomic features (log-rank test P = 0.001, <0.001, <0.001 for TC, IVC, and IEVC, respectively). The discrimination accuracies of radiomic signature for predicting recurrence risk within 18 months were 82.4% (95% CI, 77.8-87.0%), 77.3% (95% CI, 74.4-80.2%), and 79.7% (95% CI, 73.8-85.6%) for TC, IVC, and IEVC, respectively. Further, the discrimination accuracies of radiomic signature for predicting recurrence risk within 3 years were 83.4% (95% CI, 77.3-89.6%), 82.0% (95% CI, 78.9-85.1%), and 70.0% (95% CI, 63.6-76.4%) for TC, IVC, and IEVC, respectively. Finally, the accuracy of radiomic nomogram for predicting 18-month and 3-year recurrence risks were 84.1% (95% CI, 80.5-87.7%) and 88.9% (95% CI, 85.8-92.5%), respectively. Conclusions: Radiomic signature and radiomic nomogram may be low-cost, non-invasive means for successfully predicting risk for postoperative advanced HGSOC recurrence before or during the perioperative period. Radiomic signature is a potential prognostic marker that may allow for individualized evaluation of patients with advanced HGSOC.

Keywords: CT; advanced high-grade serous ovarian cancer; prognosis; radiomics; recurrence.

Figure 1

Eligibility criteria. Flowchart depicting the patient selection process. WCSUH-SCU, West China Second University Hospital of Sichuan University; HNPPH, Henan Provincial People's Hospital; HGSOC, High-Grade Serous Ovarian Cancer; FIGO, International Federation of Gynecology and Obstetrics; PACS, Picture Archiving and Communication System.

Figure 2

Study flowchart. LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; K-M, Kaplan-Meier.

Figure 3

Clinical recurrence-free survival stratified by risk according to radiomic signature. Kaplan-Meier curves showing clinical recurrence-free survival in patients stratified by radiomic signature risk and classification in the WCSUH-SCU training cohort (A), the WCSUH-SCU internal validation cohort (B), and the HNPPH independent external validation cohort (C). High-risk and low-risk curves were compared with the log-rank test. WCSUH-SCU, West China Second University Hospital of Sichuan University; HNPPH, Henan Provincial People's Hospital.

Figure 4

Time-dependent ROC curve and calibration curves. Time-dependent ROC curve for the radiomic signature predicting 3-year (A) PFS and 18-month (B) PFS in the WCSUH-SCU training cohort, WCSUH-SCU internal validation cohort, and the HNPPH independent external validation cohort. Time-dependent ROC curve for the radiomic nomogram predicting 3-year (C) PFS and 18-month (D) PFS in the training cohort compared with the predictive models based on clinical characteristics. Calibration curves of 3-year (E) and 18-month (F) time-dependent ROC curve of radiomic nomogram and radiomic signature. ROC curve, receiver operating characteristic curve; WCSUH-SCU, West China Second University Hospital of Sichuan University; HNPPH, Henan Provincial People's Hospital. PFS, Progress Free Survival.

Figure 5

Radiomic nomogram. Probability of 3-year and 18-month progress-free survival (PFS) in patients with advanced high-grade serous ovarian cancer using the radiomic nomogram prediction model, which was developed in a training cohort with radiomic signature and seven clinical characteristics. First, locate the radiomic signature value of a patient on the Radiomic Signature axis and draw a line straight upward to the Points axis. Second, repeat the process for each variable. Third, Sum the points of the eight risk factors. Finally, locate the final sum on the Total Point axis and draw a line straight down to find the probability of 3-year and 18-month PFS. FIGO, International Federation of Gynecology and Obstetrics; CA-125, Carbohydrate Antigen 125.