Yao Hu,1,* Jiao Ai2,* 

1Department of Obstetrics and Gynecology, Jingzhou Hospital Affiliated to Yangtze University,Jingzhou Central Hospital, Jingzhou, Hubei, People’s Republic of China; 2Department of Urology, Jingzhou Hospital Affiliated to Yangtze University, Jingzhou Central Hospital, Jingzhou, Hubei, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yao Hu, Email huyao3784609@163.com

Objective: To develop an early warning system that enables accurate parametrial invasion (PMI) risk prediction in cervical cancer patients with early-stage.
Methods: We retrospectively collected 218 early-stage cervical cancer patients who were treated in Jingzhou Central Hospital from January 31, 2015, to January 31, 2023, and diagnosed with early stage cervical cancer by pathology. The prediction model training is achieved by randomly dividing 70% of the training queue population, with the remaining 30% used as the testing queue. Then, a prediction model based on machine learning algorithms (including random forest, generalized linear regression, decision tree, support vector machine, and artificial neural network) is constructed to predict the risk of PMI occurrence. Ultimately, the analysis of receiver operating characteristic curve (ROC) and decision curve analysis (DCA) is used to evaluate the predictive ability of various prediction models.
Results: We finally included radiomics-based candidate variables that can be used for PMI model. Multivariate logistic regression analysis showed that energy, correlation, sum entropy (SUE), entropy, mean sum (MES), variance of differences (DIV), and inverse difference (IND) were independent risk factors for PMI occurrence. The predictive performance AUC of five types of machine learning ranges from 0.747 to 0.895 in the training set and can also reach a high accuracy of 0.905 in the testing set, indicating that the predictive model has ideal robustness.
Conclusion: Our ML-based model incorporating GLCM parameters can predict PMI in cervical cancer patients with stage IB1 to IIA2, particularly the RFM, which could contribute to distinguishing PMI before surgery, especially in assisting decision-making on surgical scope.

Keywords: cervical cancer, machine learning, parametrial invasion, gray level co-occurrence matrix, prediction model