Multi-task improved nnU-Net model based on enhanced CT for segmenting primary oral cancer and predicting patients' relapse free survival
10.13929/j.issn.1003-3289.2025.09.024
- VernacularTitle:基于增强CT构建多任务改进nnU-Net模型分割原发口腔癌病灶及预测患者无复发生存时间
- Author:
Huimin JIANG
1
;
Liming FANG
;
Shuhan QIU
;
Jing WU
Author Information
1. 皖南医学院医学影像学院,安徽芜湖 241002
- Publication Type:Journal Article
- Keywords:
mouth neoplasms;
artificial intelligence;
tomography,X-ray computed;
image segmentation;
relapse free survive
- From:
Chinese Journal of Medical Imaging Technology
2025;41(9):1568-1572
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of multi-task improved nnU-Net model based on enhanced CT for segmenting primary oral cancer and predicting patients'relapse free survival(RFS).Methods Enhanced CT data of 186 cases of primary oral cancer were retrospectively analyzed,and a multi-task improved nnU-Net model was constructed for tumor segmentation and survival prediction tasks.Pre-training of tumor segmentation was completed with nnU-Net as the baseline network,and the accuracy of recognizing and segmenting tumor was improved by enhancing the decoder through the modified skip connection.Then univariable and multivariable regression analyses were used to select clinical features closely associated with RFS.Radiomics and deep learning features were also extracted to construct a survival prediction model,with fine-tuning of the above model.The training set,validation set and test set were divided at a ratio of 7∶2∶1.Dice similarity coefficient(DSC)was used to evaluate the segmentation performance of the modified model,and the consistency index C-index was used to verify the performance of the improved model for predicting RFS.Results DSC of the multi-task improved nnU-Net model(0.78)for segmenting primary oral cancer was superior to that of 3D Inception ResNet(0.65),3D InceptSENet(0.75)and 3D U-Net models(0.69),respectively,its C-index for predicting RFS(0.798)was higher than that of Cox regression model(0.744),ICARE model(0.761),random forest model(0.744),DeepSurv model(0.735),nnU-Net model(0.760)and radiology+nnU-Net model(0.744),respectively.DSC for segmenting primary oral cancer and C-index for predicting RFS of multi-task improved nnU-Net model were both superior to those of simple baseline network(0.653 and 0.649),baseline network+multi-scale convolution fusion(0.755 and 0.752),as well as baseline network combined with clinical features(0.764 and 0.759),radiomics features(0.770 and 0.764)and clinical+radiomics features(0.773 and 0.761),respectively.Conclusion Multi-task improved nnU-Net model could be used to effectively improve the accuracy of tumor segmentation and predicting patients'RFS.
