Objectives:

The prediction model of para-aortic lymph node metastasis (LNM) in patients with early cervical cancer was constructed based on the logistic regression (LR) and random forest (RF) algorithms in the machine learning algorithm. The prediction efficiencies of the two models were compared.

Methods:

The clinical data of 204 patients with early cervical cancer in the First Affiliated Hospital of Guangxi Medical University were retrospectively collected. The 204 patients were randomly divided into a training set and a verification set according to a ratio of 3:1. The training set was used to build the model. The verification set was used to evaluate model effectiveness. The para-aortic LNM prediction model of early cervical cancer was established by LR and RF. Receiver operating characteristic curve (ROC), sensitivity, and specificity were used to evaluate the prediction performances of the two models.

Results:

LR analysis showed that tumor diameter > 4 cm, choroidal aneurysm embolism, pelvic lymph node metastasis, and high preoperative squamous cell carcinoma antigen (SCC-Ag) level were risk factors for para-aortic LNM in patients with early cervical cancer (P < 0.05). The area under the ROC curve (AUC) was 0.914. The sensitivity, specificity, and accuracy were 92.6%, 66.7%, 87.0%, respectively. The results of the importance analysis of the characteristic variables of the RF showed that the top 5 variables were preoperative SCC-Ag level, tumor diameter > 4 cm, advanced clinical stage, cancer thrombus, and pelvic lymph node metastasis. The AUC of the RF was 0.883. The sensitivity, specificity, and accuracy were 90.7%, 53.3%, 82.6%, respectively. There was no significant difference in AUC between the LR and RF (P > 0.05).

Conclusions:

Both LR and RF models based on machine learning algorithm have great predictive value in predicting early cervical cancer para-aortic lymph node metastasis.