Radiomics and deep learning predict neoadjuvant immunochemotherapy response in locally advanced esophageal squamous cell carcinoma

Figure 1 Patient enrollment process. ESCC: Esophageal squamous cell carcinoma; AJCC: American Joint Committee on Cancer; NICT: Neoadjuvant immunochemotherapy; CT: Computed tomography.

Figure 2 Study workflow. SVM: Support Vector Machine; KNN: K-Nearest Neighbors; MLP: Multilayer Perceptron; AUC: Area under the curve; TPR: True positive rate; FPR: False positive rate.

Figure 3  Representative gradient-weighted class activation mapping heatmaps of deep learning models for predicting neoadjuvant immunochemotherapy response.

Figure 4 Receiver operating characteristic curves of the best performing models across different feature types in training, internal validation, and external validation cohort. A: Training; B: Internal validation; C: External validation cohort. RD: Radiomics-deep learning; CRD: Clinical-radiomics-deep learning; AUC: Area under the curve; TPR: True positive rate; FPR: False positive rate.

Figure 5 Confusion matrices of the radiomics-deep learning model in the training, internal validation, and external validation cohorts at the Youden-derived threshold of 0. 4173. A: Training; B: Internal validation; C: External validation. Values are presented as counts (percentages of the cohort).

Figure 6 Decision curve analysis of the best-performing models across different feature types in the training, internal validation, and external validation cohorts. A: Training; B: Internal validation; C: External validation. RD: Radiomics-deep learning; CRD: Clinical-radiomics-deep learning.