Clinical and Experimental Obstetrics & Gynecology (Jan 2024)

Construction of a Prediction Model of Cancer-Specific Survival after Ovarian Clear Cell Carcinoma Surgery

  • Mengqi Huang,
  • Li Ling,
  • Yanbo Liu,
  • Yujuan Li

DOI
https://doi.org/10.31083/j.ceog5101025
Journal volume & issue
Vol. 51, no. 1
p. 25

Abstract

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Background: Ovarian clear cell carcinoma (OCCC) is a special pathological type of epithelial ovarian cancer (EOC). Due to its low incidence rate, there is a lack of real-world studies at present. The purpose of the study is to construct a nomogram model for predicting postoperative cancer-specific survival (CSS) of patients with OCCC and analyze in detail the risk factors associated with OCCC. To construct a nomogram model for predicting postoperative CSS of patients with OCCC and analyze in detail the risk factors associated with OCCC. Methods: The clinical pathological data of 596 OCCC patients were collected from the surveillance, epidemiology, and end results (SEER) database from 2010 to 2015. Of these patients, 420 were allocated to the training group and 176 patients to the validation group using bootstrap resampling. The nomogram was developed based on the Cox regression model for predicting the cancer-specific survival probability of patients at 3 and 5 years after the operation. The model was evaluated in both the training and validation groups using consistency index, receiver operating characteristic (ROC), and calibration plots. Results: The independent risk factors for CSS in OCCC patients included International Federation of Gynecology and Obstetrics (FIGO) stage, race, age, tumor laterality, and the log odds of positive lymph nodes (LODDS). The nomograms were established for predicting the 3-year and 5-year CSS of patients after operation. The c-index of the nomogram for CSS was 0.786 in the training group and 0.742 in the verification group. Area under the curve (AUCs) of the 3-year and 5-year ROC curves were 0.818, 0.824 in the training group; and 0.816, 0.808 in the verification group, respectively. Conclusions: Based on the real population data, the construction of the CSS prediction model after OCCC surgery has high prediction efficiency, can identify postoperative high-risk OCCC patients, and can be a valuable aid for the tumor staging system.

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