Preoperative prediction model of lymph node metastasis in the inguinal and femoral region based on radiomics and artificial intelligence.

Objective: To predict preoperative inguinal lymph node metastasis in vulvar cancer patients using a machine learning model based on imaging features and clinical data from pelvic magnetic resonance imaging (MRI).

Methods: 52 vulvar cancer patients were divided into a training set (n=37) and validation set (n=15). Clinical data and MRI images were collected, and regions of interest were delineated by experienced radiologists. A total of 1688 quantitative imaging features were extracted using the Radcloud platform. Dimensionality reduction and feature selection were applied, resulting in a radiomics signature. Clinical characteristics were screened, and a combined model integrating the radiomics signature and significant clinical features was constructed using logistic regression. Four machine learning classifiers (K nearest neighbor, random forest, adaptive boosting, and latent dirichlet allocation) were trained and validated. Model performance was evaluated using the receiver operating characteristic curve and the area under the curve (AUC), as well as decision curve analysis.

Results: The radiomics score significantly differentiated between lymph node metastasis positive and negative patients in both the training and validation sets. The combined model demonstrated excellent discrimination, with AUC values of 0.941 and 0.933 in the training and validation sets, respectively. The calibration curve and decision curve analysis confirmed the model's high predictive accuracy and clinical utility. Among the machine learning classifiers, latent dirichlet allocation and random forest models achieved AUC values >0.7 in the validation set. Integrating all four classifiers resulted in a total model with an AUC of 0.717 in the validation set.

Conclusion: Radiomics combined with artificial intelligence can provide a new method for prediction of inguinal lymph node metastasis of vulvar cancer before surgery.