The Associations and Causal Relationships of Ovarian Cancer - Construction of a Prediction Model

Abstract

Jing Liu,1,* Tingting Hu,2,3,* Yulan Guan,2,4 Jinguo Zhai2 1Department of Gynecology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, 510700, People’s Republic of China; 2School of Nursing, Southern Medical University, Guangzhou, Guangdong, 510515, People’s Republic of China; 3Department of Gynecology, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, 510180, People’s Republic of China; 4Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, Guangdong, 510105, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yulan Guan, Department of Gynecology, Guangdong Provincial Hospital of Chinese Medicine, No. 261 Datong Road, Yuexiu District, Guangzhou, 510105, People’s Republic of China, Tel [phone], Email [email protected] Jinguo Zhai, Nursing School, Southern Medical University, No. 1023-1063 Sha Tai Nan Road, Baiyun District, Guangzhou, 510515, People’s Republic of China, Tel [phone], Email [email protected]: 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.Keywords: ovarian cancer, incidence factors, decision tree model, risk prediction

Keywords

• ovarian cancer
• incidence factors
• decision tree model
• risk prediction