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Purpose <p>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.</p> Methods and materials <p>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.</p> Results <p>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.</p> Conclusions <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月13日

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  • Murat Yuce,
  • Sinem B. Erdogan,
  • Ata Akin,
  • Enis Ozyar,
  • Gorkem Gungor,
  • Bora Guvendiren,
  • Bukem Tanoren,
  • Seda Nilgun Dumlu

摘要

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.