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Background <p>Accurate preoperative risk stratification for patients with head and neck (H&amp;N) cancer remained a critical challenge, as long-term survival rates are poor despite aggressive multimodality treatment. While deep learning models showed promise for outcome prediction from medical images, their typical requirement for massive datasets presented a significant barrier to development and clinical translation. To overcome this limitation, we developed a transfer learning-based framework to accurately predict key treatment outcomes, locoregional recurrence (LR), distant metastasis (DM), and overall survival (OS), from non-invasive computed tomography (CT) images.</p> Methods <p>Our framework, OPHN-Net, utilized a VGG16 architecture pre-trained on ImageNet. The framework was trained and validated using a public dataset from The Cancer Imaging Archive, which comprises CT images and clinical data for 296 patients from four independent institutions. To overcome data limitations and class imbalance, we implemented a novel random-plane view resampling method for data augmentation. The network was trained and validated on data from two institutions and then independently tested on a cohort from the remaining two. Finally, we constructed an integrated model by combining the predictions from our imaging-based model with key clinical characteristics to further enhance performance.</p> Results <p>On the independent test cohort, our OPHN-Net framework substantially outperformed both traditional radiomics and a previously published deep learning model across all endpoints. The model achieved AUCs of 0.84 (95% CI, 0.75–0.90) for LR, 0.89 (95% CI, 0.82–0.95) for DM, and 0.79 (95% CI, 0.70–0.87) for OS. Furthermore, integrating clinical characteristics with the imaging-based predictions yielded a final model with even greater performance, boosting the AUCs to 0.87 (95% CI, 0.80–0.93) for LR, 0.91 (95% CI, 0.83–0.95) for DM, and 0.86 (95% CI, 0.78–0.92) for OS.</p> Conclusion <p>Our transfer learning-based framework, OPHN-Net, provided a robust and data-efficient method for predicting treatment outcomes in H&amp;N cancer from non-invasive CT images. The integration of imaging-based predictions with clinical characteristics created a more comprehensive prognostic model. This approach had the potential to facilitate personalized treatment stratification, ultimately leading to improved clinical decision-making and patient outcomes.</p>

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Predicting outcomes in head and neck cancer using CT images via transfer learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月14日

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  • Wenzhe Zhao,
  • Xin Huang,
  • Lei Xu

摘要

Background

Accurate preoperative risk stratification for patients with head and neck (H&N) cancer remained a critical challenge, as long-term survival rates are poor despite aggressive multimodality treatment. While deep learning models showed promise for outcome prediction from medical images, their typical requirement for massive datasets presented a significant barrier to development and clinical translation. To overcome this limitation, we developed a transfer learning-based framework to accurately predict key treatment outcomes, locoregional recurrence (LR), distant metastasis (DM), and overall survival (OS), from non-invasive computed tomography (CT) images.

Methods

Our framework, OPHN-Net, utilized a VGG16 architecture pre-trained on ImageNet. The framework was trained and validated using a public dataset from The Cancer Imaging Archive, which comprises CT images and clinical data for 296 patients from four independent institutions. To overcome data limitations and class imbalance, we implemented a novel random-plane view resampling method for data augmentation. The network was trained and validated on data from two institutions and then independently tested on a cohort from the remaining two. Finally, we constructed an integrated model by combining the predictions from our imaging-based model with key clinical characteristics to further enhance performance.

Results

On the independent test cohort, our OPHN-Net framework substantially outperformed both traditional radiomics and a previously published deep learning model across all endpoints. The model achieved AUCs of 0.84 (95% CI, 0.75–0.90) for LR, 0.89 (95% CI, 0.82–0.95) for DM, and 0.79 (95% CI, 0.70–0.87) for OS. Furthermore, integrating clinical characteristics with the imaging-based predictions yielded a final model with even greater performance, boosting the AUCs to 0.87 (95% CI, 0.80–0.93) for LR, 0.91 (95% CI, 0.83–0.95) for DM, and 0.86 (95% CI, 0.78–0.92) for OS.

Conclusion

Our transfer learning-based framework, OPHN-Net, provided a robust and data-efficient method for predicting treatment outcomes in H&N cancer from non-invasive CT images. The integration of imaging-based predictions with clinical characteristics created a more comprehensive prognostic model. This approach had the potential to facilitate personalized treatment stratification, ultimately leading to improved clinical decision-making and patient outcomes.