Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE
Abstract Background Accurate prediction of tumor response after drug-eluting beads transarterial chemoembolization (DEB-TACE) remains challenging in hepatocellular carcinoma (HCC), given tumor heterogeneity and dynamic changes over time. Existing prediction models based on single timepoint imaging d...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-08-01
|
| Series: | Cancer Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40644-025-00926-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849225965680459776 |
|---|---|
| author | Nan Wei René Michael Mathy De-Hua Chang Philipp Mayer Jakob Liermann Christoph Springfeld Michael T Dill Thomas Longerich Georg Lurje Hans-Ulrich Kauczor Mark O. Wielpütz Osman Öcal |
| author_facet | Nan Wei René Michael Mathy De-Hua Chang Philipp Mayer Jakob Liermann Christoph Springfeld Michael T Dill Thomas Longerich Georg Lurje Hans-Ulrich Kauczor Mark O. Wielpütz Osman Öcal |
| author_sort | Nan Wei |
| collection | DOAJ |
| description | Abstract Background Accurate prediction of tumor response after drug-eluting beads transarterial chemoembolization (DEB-TACE) remains challenging in hepatocellular carcinoma (HCC), given tumor heterogeneity and dynamic changes over time. Existing prediction models based on single timepoint imaging do not capture dynamic treatment-induced changes. This study aims to develop and validate a predictive model that integrates deep learning and machine learning algorithms on longitudinal contrast-enhanced MRI (CE-MRI) to predict treatment response in HCC patients undergoing DEB-TACE. Methods This retrospective study included 202 HCC patients treated with DEB-TACE from 2004 to 2023, divided into a training cohort (n = 141) and validation cohort (n = 61). Radiomics and deep learning features were extracted from standardized longitudinal CE-MRI to capture dynamic tumor changes. Feature selection involved correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator regression. The patients were categorized into two groups: the objective response group (n = 123, 60.9%; complete response = 35, 28.5%; partial response = 88, 71.5%) and the non-response group (n = 79, 39.1%; stable disease = 62, 78.5%; progressive disease = 17, 21.5%). Predictive models were constructed using radiomics, deep learning, and integrated features. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. Results We retrospectively evaluated 202 patients (62.67 ± 9.25 years old) with HCC treated after DEB-TACE. A total of 7,182 radiomics features and 4,096 deep learning features were extracted from the longitudinal CE-MRI images. The integrated model was developed using 13 quantitative radiomics features and 4 deep learning features and demonstrated acceptable and robust performance with an receiver operating characteristic curve (AUC) of 0.941 (95%CI: 0.893–0.989) in the training cohort, and AUC of 0.925 (95%CI: 0.850–0.998) with accuracy of 86.9%, sensitivity of 83.7%, as well as specificity of 94.4% in the validation set. Conclusions This study presents a predictive model based on longitudinal CE-MRI data to estimate tumor response to DEB-TACE in HCC patients. By capturing tumor dynamics and integrating radiomics features with deep learning features, the model has the potential to guide individualized treatment strategies and inform clinical decision-making regarding patient management. |
| format | Article |
| id | doaj-art-1718e2b508ab410d9cc24144b8a472ec |
| institution | Kabale University |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-1718e2b508ab410d9cc24144b8a472ec2025-08-24T11:48:20ZengBMCCancer Imaging1470-73302025-08-0125111310.1186/s40644-025-00926-5Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACENan Wei0René Michael Mathy1De-Hua Chang2Philipp Mayer3Jakob Liermann4Christoph Springfeld5Michael T Dill6Thomas Longerich7Georg Lurje8Hans-Ulrich Kauczor9Mark O. Wielpütz10Osman Öcal11Department of Diagnostic and Interventional Radiology, University Hospital of HeidelbergDepartment of Diagnostic and Interventional Radiology, University Hospital of HeidelbergDepartment of Diagnostic and Interventional Radiology, University Hospital of HeidelbergDepartment of Diagnostic and Interventional Radiology, University Hospital of HeidelbergLiver Cancer Center HeidelbergLiver Cancer Center HeidelbergLiver Cancer Center HeidelbergNational Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Hospital of HeidelbergNational Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Hospital of HeidelbergDepartment of Diagnostic and Interventional Radiology, University Hospital of HeidelbergDepartment of Diagnostic and Interventional Radiology, University Hospital of HeidelbergDepartment of Diagnostic and Interventional Radiology, University Hospital of HeidelbergAbstract Background Accurate prediction of tumor response after drug-eluting beads transarterial chemoembolization (DEB-TACE) remains challenging in hepatocellular carcinoma (HCC), given tumor heterogeneity and dynamic changes over time. Existing prediction models based on single timepoint imaging do not capture dynamic treatment-induced changes. This study aims to develop and validate a predictive model that integrates deep learning and machine learning algorithms on longitudinal contrast-enhanced MRI (CE-MRI) to predict treatment response in HCC patients undergoing DEB-TACE. Methods This retrospective study included 202 HCC patients treated with DEB-TACE from 2004 to 2023, divided into a training cohort (n = 141) and validation cohort (n = 61). Radiomics and deep learning features were extracted from standardized longitudinal CE-MRI to capture dynamic tumor changes. Feature selection involved correlation analysis, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator regression. The patients were categorized into two groups: the objective response group (n = 123, 60.9%; complete response = 35, 28.5%; partial response = 88, 71.5%) and the non-response group (n = 79, 39.1%; stable disease = 62, 78.5%; progressive disease = 17, 21.5%). Predictive models were constructed using radiomics, deep learning, and integrated features. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the models. Results We retrospectively evaluated 202 patients (62.67 ± 9.25 years old) with HCC treated after DEB-TACE. A total of 7,182 radiomics features and 4,096 deep learning features were extracted from the longitudinal CE-MRI images. The integrated model was developed using 13 quantitative radiomics features and 4 deep learning features and demonstrated acceptable and robust performance with an receiver operating characteristic curve (AUC) of 0.941 (95%CI: 0.893–0.989) in the training cohort, and AUC of 0.925 (95%CI: 0.850–0.998) with accuracy of 86.9%, sensitivity of 83.7%, as well as specificity of 94.4% in the validation set. Conclusions This study presents a predictive model based on longitudinal CE-MRI data to estimate tumor response to DEB-TACE in HCC patients. By capturing tumor dynamics and integrating radiomics features with deep learning features, the model has the potential to guide individualized treatment strategies and inform clinical decision-making regarding patient management.https://doi.org/10.1186/s40644-025-00926-5Deep learningMachine learningSiamese networkHCCTumor response |
| spellingShingle | Nan Wei René Michael Mathy De-Hua Chang Philipp Mayer Jakob Liermann Christoph Springfeld Michael T Dill Thomas Longerich Georg Lurje Hans-Ulrich Kauczor Mark O. Wielpütz Osman Öcal Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE Cancer Imaging Deep learning Machine learning Siamese network HCC Tumor response |
| title | Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE |
| title_full | Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE |
| title_fullStr | Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE |
| title_full_unstemmed | Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE |
| title_short | Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE |
| title_sort | longitudinal ce mri based siamese network with machine learning to predict tumor response in hcc after deb tace |
| topic | Deep learning Machine learning Siamese network HCC Tumor response |
| url | https://doi.org/10.1186/s40644-025-00926-5 |
| work_keys_str_mv | AT nanwei longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT renemichaelmathy longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT dehuachang longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT philippmayer longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT jakobliermann longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT christophspringfeld longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT michaeltdill longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT thomaslongerich longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT georglurje longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT hansulrichkauczor longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT markowielputz longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace AT osmanocal longitudinalcemribasedsiamesenetworkwithmachinelearningtopredicttumorresponseinhccafterdebtace |