BMC cancer · 2025
Deep learning radiomics based on MRI for differentiating tongue cancer T - staging.
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Lu Zhaoyi, Zhu Bowen, Ling Hang, Chen Xi
Аннотация
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.
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.