Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE.
Authors
Affiliations (13)
Affiliations (13)
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
- Liver Cancer Center Heidelberg, Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
- National Center for Tumor Diseases (NCT), NCT Heidelberg, a partnership between DKFZ and University Hospital of Heidelberg, Heidelberg, Germany.
- Institute of Radiology and Nuclear Medicine, Cantonal Hospital Lucerne, Spitalstrasse, Lucerne, CH-6000, Switzerland.
- Department of Radiation Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany.
- Department of Medical Oncology, University Hospital of Heidelberg, Im Neuenheimer Feld 460, 69120, Heidelberg, Germany.
- Department of Gastroenterology, Hepatology, Infectious Diseases and Intoxication, University Hospital of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
- German Cancer Research Center (DKFZ) Heidelberg, Experimental Hepatology, Inflammation and Cancer, Heidelberg, Germany.
- Institute of Pathology, University Hospital of Heidelberg, Im Neuenheimer Feld 410, 69120, Heidelberg, Germany.
- Department of General, Visceral and Transplantation Surgery, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany.
- Department of Diagnostic and Interventional Radiology, University Hospital of Heidelberg, Im Neuenheimer Feld 420, 69120, Heidelberg, Germany. [email protected].
- Department of Diagnostic Radiology and Neuroradiology, University Medicine Greifswald, Ferdinand-Sauerbruch-Strasse 1, 17475, Greifswald, Germany. [email protected].
- Clinic for Nuclear Medicine, University Medicine Greifswald, Ferdinand-Sauerbruch-Strasse 1, 17475, Greifswald, Germany. [email protected].
Abstract
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. This retrospective study included 202 HCC patients treated with DEB-TACE from 2004 to 2023, divided into a training cohort (<i>n</i> = 141) and validation cohort (<i>n</i> = 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 (<i>n</i> = 123, 60.9%; complete response = 35, 28.5%; partial response = 88, 71.5%) and the non-response group (<i>n</i> = 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. 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. 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. The online version contains supplementary material available at 10.1186/s40644-025-00926-5.