Researchers have developed a new prediction tool that could help clinicians better assess whether complete tumour removal is achievable in women with advanced epithelial ovarian cancer, potentially improving treatment planning and outcomes.
The study focused on patients with stage IIB to IV disease, where achieving an R0 resection, meaning no visible residual tumour after surgery, is one of the strongest predictors of long-term survival. However, determining the likelihood of complete resection before surgery remains a major clinical challenge.
Study Design And Patient Cohorts
The research analysed data from 209 women treated at three independent medical institutions. Patients were divided into a training cohort of 144 cases from two centres and an independent validation cohort of 65 cases from a third centre. All participants had undergone preoperative CT imaging and routine clinical testing within two weeks before debulking surgery.
Using the training cohort, investigators performed univariate and multivariate logistic regression analyses to identify which CT-based and clinical features were independently associated with achieving R0 resection. These predictors were then incorporated into a nomogram, a visual statistical tool designed for use in clinical decision-making.
R0 resection was achieved in 66% of patients in the training cohort and 61.5% of patients in the validation cohort, reflecting the real-world difficulty of complete tumour removal in advanced ovarian cancer.
Key Predictors Of Surgical Outcomes
Three factors emerged as independent predictors of successful R0 resection. These were the overall peritoneal cancer index derived from CT imaging, serum levels of human epididymis protein 4, and the neutrophil-to-lymphocyte ratio, a marker of systemic inflammation.
Higher CT-based peritoneal cancer burden, elevated HE4 levels, and increased inflammatory markers were all associated with a reduced likelihood of complete tumour removal. Together, these variables provided a comprehensive picture of tumour spread and the patient’s biological response to disease.
Clinical Value Of The Nomogram
The resulting nomogram demonstrated strong predictive performance. In the training cohort, the model achieved an area under the receiver operating characteristic curve of 0.908, indicating excellent accuracy. Performance remained robust in the independent validation cohort, with an area under the curve of 0.779. Calibration analyses showed good agreement between predicted probabilities and actual surgical outcomes in both groups.
The researchers conclude that this CT- and blood-based nomogram could serve as a reliable, non-invasive tool to support preoperative decision-making. By identifying patients most likely to achieve complete resection, the model may help clinicians tailor surgical strategies and consider alternative approaches, such as neoadjuvant chemotherapy, when appropriate.
Reference
Liu X et al. Nomogram based on CT and clinical features to predict R0 resection in patients with stage IIB–IV epithelial ovarian cancer: a multi-center study. Scientific Reports. 2025; https://doi.org/10.1038/s41598-025-33657-5.
