Transcriptome-based Deep Learning Model for Predicting Gemcitabine and Cisplatin Chemotherapy Response in Urothelial Carcinoma: Development and External Validation

Performance evaluation of the deep learning model in internal and external validation. (A-C) Internal validation results: (A) ROC curves are presented for the training and internal validation sets; (B) Training and validation loss curves over 200 epochs showing stable convergence; (C) Precision, recall, and F1-score metrics across different threshold values with optimal threshold (0.4) indicated by the vertical dashed line. (D-G) External validation results: (D) ROC curves for training and external test sets with respective AUROCs; (E) Loss curves during external validation showing good generalization; (F) Detailed performance metrics for each fold during 5-fold cross-validation; (G) Final performance metrics and confusion matrix of the model in training and external validation datasets. AUROC: Area under the receiver operating characteristic curve; ROC, receiver operating characteristic.

Gene ontology enrichment analysis of chemotherapy response-associated genes. (A) Bar plots showing enriched biological process terms in four major functional clusters. The x-axis represents negative log2-transformed p-Values, and bars are colored by gene ontology category. (B) Chord diagram illustrating the relationships between representative genes and their associated biological processes.