Multi-institutional deep learning for GTV segmentation and survival prediction in nasopharyngeal carcinoma.

PURPOSE: To develop an automated workflow for gross tumor volume (GTV) segmentation in radiotherapy planning CT images of nasopharyngeal carcinoma (NPC) patients and to evaluate the 5-year Disease-Free Survival (DFS) predictive performance of radiomics, clinical and combined features. METHODS AND MATERIALS: Contrast-enhanced planning CTs of 75 NPC patients were collected. SwinUNETR, UNETR and nnU-Net models were trained with five-fold cross-validation; performance was quantified by Dice similarity coefficient (DSC). Additional 120 scans from SeGrap2023 were incorporated to assess the model performances on diverse cohorts. For DFS prediction, 1059 slice-wise radiomic features were extracted. Feature selection used univariate filtering, correlation thresholding and LASSO, followed by machine-learning modelling with radiomic, clinical and combined inputs.

RESULTS

The highest 5-fold cross-validation DSC performance was achieved by nnU-Net (DSC = 0.79) when trained and internally validated only on the SeGrap2023 dataset. However, DSC dropped to 0.36 when Acibadem cohort data were utilized for external test set, suggesting a significant effect of the domain shift. When the Acibadem and SeGrap2023 datasets were combined, nnU-Net achieved an average DSC of 0.73 in 5-fold cross-validation and 0.69 on subset of Acibadem internal test cases. In predicting 5-year DFS, a Logistic Regression model using combined radiomic and clinical features provided the highest AUC score (0.79), outperforming clinical-only (AUC = 0.59) and radiomics-only (AUC = 0.63) feature sets.

CONCLUSIONS

Multi-institutional training mitigates domain shift and boosts segmentation robustness. In addition, integrating radiomics with clinical data enhances DFS prediction in NPC. Advanced deep-learning and machine-learning pipelines can refine radiotherapy planning and prognostication, supporting personalized management and improved outcomes.