Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study
Abstract Objectives This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepatocellular carcinoma (HCC). Methods Utilizing a retrospective dataset of 546 HCC patients from five centers, we developed and validated a clin...
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SpringerOpen
2025-07-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-02035-0 |
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| author | Xue Dong Xibin Jia Wei Zhang Jingxuan Zhang Hui Xu Lixue Xu Caili Ma Hongjie Hu Jiawen Luo Jingfeng Zhang Zhenchang Wang Wenbin Ji Dawei Yang Zhenghan Yang |
| author_facet | Xue Dong Xibin Jia Wei Zhang Jingxuan Zhang Hui Xu Lixue Xu Caili Ma Hongjie Hu Jiawen Luo Jingfeng Zhang Zhenchang Wang Wenbin Ji Dawei Yang Zhenghan Yang |
| author_sort | Xue Dong |
| collection | DOAJ |
| description | Abstract Objectives This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepatocellular carcinoma (HCC). Methods Utilizing a retrospective dataset of 546 HCC patients from five centers, we developed and validated a clinical-radiological model and deep learning models aimed at MVI prediction. The models were developed on a dataset of 263 cases consisting of data from three centers, internally validated on a set of 66 patients, and externally tested on two independent sets. An adversarial network-based deep learning (AD-DL) model was developed to learn domain-invariant features from multiple centers within the training set. The area under the receiver operating characteristic curve (AUC) was calculated using pathological MVI status. With the best-performed model, early recurrence-free survival (ERFS) stratification was validated on the external test set by the log-rank test, and the differentially expressed genes (DEGs) associated with MVI status were tested on the RNA sequencing analysis of the Cancer Imaging Archive. Results The AD-DL model demonstrated the highest diagnostic performance and generalizability with an AUC of 0.793 in the internal test set, 0.801 in external test set 1, and 0.773 in external test set 2. The model’s prediction of MVI status also demonstrated a significant correlation with ERFS (p = 0.048). DEGs associated with MVI status were primarily enriched in the metabolic processes and the Wnt signaling pathway, and the epithelial-mesenchymal transition process. Conclusions The AD-DL model allows preoperative MVI prediction and ERFS stratification in HCC patients, which has a good generalizability and biological interpretability. Critical relevance statement The adversarial network-based deep learning model predicts MVI status well in HCC patients and demonstrates good generalizability. By integrating bioinformatics analysis of the model’s predictions, it achieves biological interpretability, facilitating its clinical translation. Key Points Current MVI assessment models for HCC lack interpretability and generalizability. The adversarial network-based model's performance surpassed clinical radiology and squeeze-and-excitation network-based models. Biological function analysis was employed to enhance the interpretability and clinical translatability of the adversarial network-based model. Graphical Abstract |
| format | Article |
| id | doaj-art-bc02b53d99ab48b7b764b02c02085a4a |
| institution | Kabale University |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-bc02b53d99ab48b7b764b02c02085a4a2025-08-20T03:38:18ZengSpringerOpenInsights into Imaging1869-41012025-07-0116111310.1186/s13244-025-02035-0Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter studyXue Dong0Xibin Jia1Wei Zhang2Jingxuan Zhang3Hui Xu4Lixue Xu5Caili Ma6Hongjie Hu7Jiawen Luo8Jingfeng Zhang9Zhenchang Wang10Wenbin Ji11Dawei Yang12Zhenghan Yang13Department of Radiology, Beijing Friendship Hospital, Capital Medical UniversityCollege of Computer Science, Beijing University of TechnologyCollege of Computer Science, Beijing University of TechnologyDepartment of Radiology, Beijing Friendship Hospital, Capital Medical UniversityDepartment of Radiology, Beijing Friendship Hospital, Capital Medical UniversityDepartment of Radiology, Beijing Friendship Hospital, Capital Medical UniversityDepartment of Radiology, Beijing Longfu HospitalDepartment of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of MedicineDepartment of Radiology, The Second Hospital of Dalian Medical UniversityDepartment of Radiology, Ningbo No. 2 HospitalDepartment of Radiology, Beijing Friendship Hospital, Capital Medical UniversityDepartment of Radiology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical UniversityDepartment of Radiology, Beijing Friendship Hospital, Capital Medical UniversityDepartment of Radiology, Beijing Friendship Hospital, Capital Medical UniversityAbstract Objectives This study aimed to develop an interpretable, domain-generalizable deep learning model for microvascular invasion (MVI) assessment in hepatocellular carcinoma (HCC). Methods Utilizing a retrospective dataset of 546 HCC patients from five centers, we developed and validated a clinical-radiological model and deep learning models aimed at MVI prediction. The models were developed on a dataset of 263 cases consisting of data from three centers, internally validated on a set of 66 patients, and externally tested on two independent sets. An adversarial network-based deep learning (AD-DL) model was developed to learn domain-invariant features from multiple centers within the training set. The area under the receiver operating characteristic curve (AUC) was calculated using pathological MVI status. With the best-performed model, early recurrence-free survival (ERFS) stratification was validated on the external test set by the log-rank test, and the differentially expressed genes (DEGs) associated with MVI status were tested on the RNA sequencing analysis of the Cancer Imaging Archive. Results The AD-DL model demonstrated the highest diagnostic performance and generalizability with an AUC of 0.793 in the internal test set, 0.801 in external test set 1, and 0.773 in external test set 2. The model’s prediction of MVI status also demonstrated a significant correlation with ERFS (p = 0.048). DEGs associated with MVI status were primarily enriched in the metabolic processes and the Wnt signaling pathway, and the epithelial-mesenchymal transition process. Conclusions The AD-DL model allows preoperative MVI prediction and ERFS stratification in HCC patients, which has a good generalizability and biological interpretability. Critical relevance statement The adversarial network-based deep learning model predicts MVI status well in HCC patients and demonstrates good generalizability. By integrating bioinformatics analysis of the model’s predictions, it achieves biological interpretability, facilitating its clinical translation. Key Points Current MVI assessment models for HCC lack interpretability and generalizability. The adversarial network-based model's performance surpassed clinical radiology and squeeze-and-excitation network-based models. Biological function analysis was employed to enhance the interpretability and clinical translatability of the adversarial network-based model. Graphical Abstracthttps://doi.org/10.1186/s13244-025-02035-0Hepatocellular carcinomaMicrovascular invasionDeep learning modelGeneralizationInterpretability |
| spellingShingle | Xue Dong Xibin Jia Wei Zhang Jingxuan Zhang Hui Xu Lixue Xu Caili Ma Hongjie Hu Jiawen Luo Jingfeng Zhang Zhenchang Wang Wenbin Ji Dawei Yang Zhenghan Yang Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study Insights into Imaging Hepatocellular carcinoma Microvascular invasion Deep learning model Generalization Interpretability |
| title | Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study |
| title_full | Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study |
| title_fullStr | Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study |
| title_full_unstemmed | Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study |
| title_short | Interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on MRI: a multicenter study |
| title_sort | interpretable and generalizable deep learning model for preoperative assessment of microvascular invasion and outcome in hepatocellular carcinoma based on mri a multicenter study |
| topic | Hepatocellular carcinoma Microvascular invasion Deep learning model Generalization Interpretability |
| url | https://doi.org/10.1186/s13244-025-02035-0 |
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