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Objective <p>To develop a deep learning-based MRI model for predicting tongue cancer T-stage.</p> Methods <p>This retrospective study analyzed clinical and MRI data from 579 tongue cancer patients (Xiangya Cancer Hospital and Jiangsu Province Hospital). T2-weighted (T2WI) and contrast-enhanced T1-weighted (CET1) sequences were preprocessed (anonymization/resampling/calibration). Regions of interest (ROI) were segmented by two radiologists (intraclass correlation coefficient (ICC) &gt; 0.75), and using PyRadiomics, 2375 radiomics features were extracted. ResNet18 and ResNet50 algorithms were employed to build deep learning models (deep learning radiomics (DLR) resnet18 / DLRresnet50), compared with a radiomics model (Rad) based on 17 optimized features. Performance was evaluated via AUC, DCA, IDI, and NRI in different sets.</p> Results <p>In training set, deep learning models outperformed Rad (AUC: DLRresnet18 = 0.837, DLRresnet50 = 0.847 vs. Rad = 0.828). Test set and and external validation set results were consistent (DLRresnet18, AUC = 0.805 / 0.857; DLRresnet50, AUC = 0.810 / 0.860). The decision curve analysis (DCA) demonstrated that both deep learning models performed better than the Rad model in the training set, test set, and external validation set. Furthermore, both NRI and IDI of the two deep learning models compared with the Rad model were greater than 0.</p> Conclusion <p>DLRresnet18 and DLRresnet50 models significantly improve T-stage prediction accuracy over traditional radiomics, reducing subjective interpretation errors and supporting personalized treatment planning. This research achievement provides new ideas and tools for image-assisted diagnosis of tongue cancer T-stage.</p> Level of evidence <p>III.</p>

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Deep learning radiomics based on MRI for differentiating tongue cancer T - staging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月22日

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  • Zhaoyi Lu,
  • Bowen Zhu,
  • Hang Ling,
  • Xi Chen

摘要

Objective

To develop a deep learning-based MRI model for predicting tongue cancer T-stage.

Methods

This retrospective study analyzed clinical and MRI data from 579 tongue cancer patients (Xiangya Cancer Hospital and Jiangsu Province Hospital). T2-weighted (T2WI) and contrast-enhanced T1-weighted (CET1) sequences were preprocessed (anonymization/resampling/calibration). Regions of interest (ROI) were segmented by two radiologists (intraclass correlation coefficient (ICC) > 0.75), and using PyRadiomics, 2375 radiomics features were extracted. ResNet18 and ResNet50 algorithms were employed to build deep learning models (deep learning radiomics (DLR) resnet18 / DLRresnet50), compared with a radiomics model (Rad) based on 17 optimized features. Performance was evaluated via AUC, DCA, IDI, and NRI in different sets.

Results

In training set, deep learning models outperformed Rad (AUC: DLRresnet18 = 0.837, DLRresnet50 = 0.847 vs. Rad = 0.828). Test set and and external validation set results were consistent (DLRresnet18, AUC = 0.805 / 0.857; DLRresnet50, AUC = 0.810 / 0.860). The decision curve analysis (DCA) demonstrated that both deep learning models performed better than the Rad model in the training set, test set, and external validation set. Furthermore, both NRI and IDI of the two deep learning models compared with the Rad model were greater than 0.

Conclusion

DLRresnet18 and DLRresnet50 models significantly improve T-stage prediction accuracy over traditional radiomics, reducing subjective interpretation errors and supporting personalized treatment planning. This research achievement provides new ideas and tools for image-assisted diagnosis of tongue cancer T-stage.

Level of evidence

III.