Prediction of Cervical Cancer Lymph Node Metastasis via a Multimodal Transfer Learning Approach

Abstract

Aims/Background In the treatment of patients with cervical cancer, lymph node metastasis (LNM) is an important indicator for stratified treatment and prognosis of cervical cancer. This study aimed to develop and validate a multimodal model based on contrast-enhanced multiphase computed tomography (CT) images and clinical variables to accurately predict LNM in patients with cervical cancer. Methods This study included 233 multiphase contrast-enhanced CT images of patients with pathologically confirmed cervical malignancies treated at the Affiliated Dongyang Hospital of Wenzhou Medical University. A three-dimensional MedicalNet pre-trained model was used to extract features. Minimum redundancy-maximum correlation, and least absolute shrinkage and selection operator regression were used to screen the features that were ultimately combined with clinical candidate predictors to build the prediction model. The area under the curve (AUC) was used to assess the predictive efficacy of the model. Results The results indicate that the deep transfer learning model exhibited high diagnostic performance within the internal validation set, with an AUC of 0.82, accuracy of 0.88, sensitivity of 0.83, and specificity of 0.89. Conclusion We constructed a comprehensive, multiparameter model based on the concept of deep transfer learning, by pre-training the model with contrast-enhanced multiphase CT images and an array of clinical variables, for predicting LNM in patients with cervical cancer, which could aid the clinical stratification of these patients via a noninvasive manner.