Abstract
Purpose
To explore the risk and protective factors for developing ovarian cancer and construct a risk prediction model.
Methods
Information related to patients diagnosed with ovarian cancer on the electronic medical record data platform of three tertiary hospitals in Guangdong Province from May 2018 to September 2023 was collected as the case group. Patients with non-ovarian cancer who attended the clinic during the same period were included in the control group. Logistic regression analysis was used to screen the independent variables and explore the factors associated with the development of ovarian cancer. An ovarian cancer risk prediction model was constructed using a decision tree C4.5 algorithm. The ROC and calibration curves were plotted, and the model was validated.
Results
Logistic regression analysis identified independent risk and protective factors for ovarian cancer. The sample size was divided into training and test sets in a ratio of 7:3 for model construction and validation. The AUC of the training and test sets of the decision tree model were 0.961 (95% CI:0.944–0.978) and 0.902 (95% CI:0.840–0.964), respectively, and the optimal cut-off values and their coordinates were 0.532 (0.091, 0.957), and 0.474 (0.159, 0.842) respectively. The accuracies of the training and test sets were 93.3% and 84.2%, respectively, and their sensitivities were 95.7% and 84.2%, respectively.
Conclusion
The constructed ovarian cancer risk prediction model has good predictive ability, which is conducive to improving the efficiency of early warning of ovarian cancer in high-risk groups.
Abbreviations
OC, Ovarian cancer; ROMA, Risk of malignancy algorithm; miRNAs, microRNAs; ROC, receiver operating characteristic; HRT, hormone replacement therapy; CLC, Clear Cell.
Data Sharing Statement
The datasets generated and analyzed in the present study are available from the corresponding author Jinguo Zhai upon reasonable request.
Ethics Approval and Consent to Participate
The study complied with medical ethics standards and was reviewed by the Ethics Committee of Guangdong Provincial Hospital of Chinese Medicine (review approval number: ZE2023-291). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Acknowledgments
Yulan Guan and Jinguo Zhao are the co-corresponding authors. The paper has been edited and proofread by Medjaden Inc.
Disclosure
The authors report no conflicts of interest in this work.