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. 2025 Aug 12;15:1573687. doi: 10.3389/fonc.2025.1573687

Figure 2.

Receiver operating characteristic (ROC) curves display results from conventional and deep learning radiomics. Panels A and C show training data, and Panels B and D show validation data. Each panel plots sensitivity versus one minus specificity for different models, including Logistic Regression, SVM, Random Forest, Extra Trees, XGBoost, LightGBM, and MLP, with accompanying AUC values and confidence intervals.

Receiver operating characteristic (ROC) curve analyses, conducted on the training set, for accurately predicting lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC) patients. (A) ROC curves in training set for 7 conventional radiomics models, based on the machine learning algorithms of logistic regression (LR), support vector machine (SVM), random forest (RF), ExtraTrees, XGBoost, light gradient boosting machine (GBM), and feed-forward neural network multilayer perceptron (MLP), (B) ROC curves in validation for 7 conventional radiomics models, based on the machine learning algorithms of logistic regression (LR), support vector machine (SVM), random forest (RF), ExtraTrees, XGBoost, light gradient boosting machine (GBM), and feed-forward neural network multilayer perceptron (MLP), (C) ROC curves in training set for 7 deep learning radiomics (DLR) models, comprising of LR, SVM, RF, ExtraTrees, XGBoost, Light GBM, and MLP radiomics models combined with deep learning features, (D) ROC curves in validation set for 7 deep learning radiomics (DLR) models, comprising of LR, SVM, RF, ExtraTrees, XGBoost, Light GBM, and MLP radiomics models combined with deep learning features.