A Prognostic Model using FIGO 2018 Staging and MRI- derived Tumor Volume to Predict Long-term Outcomes in Patients with Uterine Cervical Squamous Cell Carcinoma Who Received Definitive Radiotherapy

Abstract

Background Uterine cervical carcinoma is a severe health threat worldwide, especially in China. The International Federation of Gynecology and Obstetrics (FIGO) has revised the staging system with emphasise the strength of magnetic resonance imaging (MRI). We aim to investigate long-term prognostic factors for FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma following definitive radiotherapy and establish a prognostic model using MRI-derived tumor volume. Methods Patients were restaged according to the FIGO 2018 staging system and randomly grouped into training and validation cohorts (7:3ratio). Optimal cutoff values of squamous cell carcinoma antigen (SCC-Ag) and tumor volume derived from MRI for the training cohort were generated. A nomogram was onstructed based on overall survival (OS) predictors, which were selected using univariate and multivariate analyses. The performance of the nomogram was validated and compared with the FIGO 2018 staging system. Risk stratification cutoff points were generated, and survival curves of low-risk and high-risk groups were compared. Results We enrolled 396 patients (training set, 277; validation set,119). The SCC-Ag and MRI-derived tumor volume cutoff values were 11.5 ng/mL and 28.85 cm3, respectively. A nomogram was established based on significant prognostic factors, including SCC-Ag, poor differentiation, tumor volume, chemotherapy, and FIGO 2018 stage. Decision curve analysis indicated the net benefits of our model were higher. The high-risk group had significantly shorter OS than the low-risk group in both the training (p < 0.0001) and validation sets (p = 0.00055). Conclusions Our nomogram predicted long-term outcomes in patients with FIGO 2018 stage II-IIIC2r uterine cervical squamous cell carcinoma. This tool could assist both gynecologic oncologists and patients in treatment planning and prognosis.