Nomogram Predicting Overall Survival in Patients with FIGO II to III Squamous Cell Cervical Carcinoma Under Radical Radiotherapy: A Retrospective Analysis Based on 2018 FIGO Staging

Abstract

Purpose To present a nomogram to predict overall survival in patients with FIGO-2018 II to III squamous cell cervical carcinoma undergoing radical radiotherapy. Patients and Methods Patients diagnosed with FIGO-2018 II to III squamous cell cervical cancer between December 2013 and December 2014 were analyzed retrospectively. The optimal cutoff point for tumor length and width were determined by R package. We identified prognostic factors by univariate and multivariate Cox proportional-hazard regression, then built a nomogram to visualize the prediction model. Our model was compared to the 2018 FIGO staging prediction model. Harrell’s concordance index, receiver operating characteristic curve, calibration plot were used to evaluate the discriminability and accuracy of the predictive models, and decision curve analysis (DCA) was used to show the net benefits. Results Data from 469 patients were included in the final analyses. The cutoff values of tumor length and width were 5.10 cm and 4.13 cm, respectively. Four independent prognostic variables—tumor length, tumor width, lower one-third vaginal involvement, and lymph node metastases—were used to establish the nomogram. The C-index of the nomogram was 0.71 (95%, CI = 0.66–0.77), which was better than that of the 2018 FIGO stage prediction model (C-index: 0.62, 95% CI = 0.58–0.66, p = 0.009). The calibration plot of the nomogram was a good fit for both 3-year and 5-year overall survival predictions. And DCA curves showed that net benefits for our model were higher than FIGO-2018 staging system. Conclusion A clinically useful nomogram for calculating overall survival probability in FIGO-2018 II to III squamous cell cervical cancer patients who had received radical radiotherapy was developed. Tumor length, tumor width, lower one-third vaginal involvement, and lymph node metastases were found to be independent prognostic factors. Our model performed better than the 2018 FIGO staging model. The findings could help clinicians in China to predict the survival of these patients in clinical care and research.

Machine Learning of Dose-Volume Histogram Parameters Predicting Overall Survival in Patients with Cervical Cancer Treated with Definitive Radiotherapy

“…Three important factors were selected, including baseline hemoglobin level, primary tumor volume (GTV_P), and body V 5. The final multivariable Cox-pH model constructed using these important factors had prediction performance (C-index: 0.78, 95%CI: 0.73–0.81) better than previous studies [ 25 – 27 ]. It indicated that the addition of DVH parameters to clinical factors in the model improved the prediction ability for OS.…”

Nomogram models for the prognosis of cervical cancer: A SEER-based study

“…In addition, we compared FIGO staging and the nomograms with respect to their net benefit. FIGO stage is widely used clinically, and the use of a nomogram could reduce the diversity due to different treatments and sociodemographic statuses when predicting the prognosis of patients with CC ( 25 , 32 ). We found that the nomograms had greater net benefit than the FIGO stage according to DCA and CIC.…”

“…Previously, many studies developed nomograms for diagnosis and prognostic prediction in patients with CC; however, these studies had many limitations, including insufficient sample size (7), the model having a low C-index and prediction accuracy (6,32), insufficient inclusion and exclusion criteria (6,33), and a single study endpoint (8,9). To our knowledge, compared with other studies evaluating CC survival using nomograms (6)(7)(8)(9), the present study considered more real-time sample data and a more complete set of patient prognostic factors (13) than previous studies, showed excellent predictive accuracy for OS and CSS (6)(7)(8)(9)33), and demonstrated greater clinical net benefit.…”

Assessing survival time of outpatients with cervical cancer: at the university of Gondar referral hospital using the Bayesian approach

“…When predicting the prognosis of patients with CC, the FIGO stage is frequently employed therapeutically, and the adoption of a nomogram could reduce the diversity caused by various treatments and socio-demographic statuses [ 44 ]. In our study, as the FIGO stage for outpatients increased, the survival time decreased.…”