3D Fractal Analysis of Gd-EOB-DTPA-MRI for Vessels Encapsulating Tumor Clusters Prediction in Hepatocellular
Miaomiao Wang1,2, Yinzhong Wang2, Ya Shen3
1The First Clinical Medical College of Lanzhou University, No.1 Donggang West Road, Lanzhou City, Gansu Province, China.
Insights
Three-dimensional fractal analysis of Gd-EOB-DTPA-MRI can predict the vessels encapsulating tumor clusters (VETC) pattern in hepatocellular carcinoma (HCC). Combining fractal parameters with clinical data improves prediction accuracy and identifies high-risk HCC patients for better outcomes.
Area of Science:
- Radiology and Medical Imaging
- Oncology
- Computational Pathology
Background:
- Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide.
- The vessels encapsulating tumor clusters (VETC) pattern is a histopathological feature associated with poor prognosis in HCC.
- Accurate preoperative prediction of the VETC pattern is crucial for treatment planning and patient management.
Purpose of the Study:
- To evaluate the efficacy of 3-dimensional (3D) fractal analysis using Gd-EOB-DTPA-MRI in predicting the VETC pattern in HCC.
- To assess the added value of fractal parameters when combined with clinical and radiological features for VETC pattern prediction.
- To investigate the association between the VETC pattern and recurrence-free survival (RFS) in HCC patients.
Main Methods:
- Retrospective analysis of 212 HCC patients who underwent preoperative Gd-EOB-DTPA-MRI.
- Extraction of fractal dimension (FD) and lacunarity from arterial and hepatobiliary phases using the box-counting method.
- Logistic regression for VETC pattern prediction and Kaplan-Meier analysis for RFS, with model performance assessed by AUC.
Main Results:
- Higher FD and lacunarity values were observed in VETC-positive HCC compared to VETC-negative HCC (P < 0.05).
- The fractal feature model achieved an AUC of 0.76; a hybrid model combining fractal parameters with AFP, capsule, and intratumoral necrosis reached an AUC of 0.80.
- VETC pattern, along with AST/ALT ratio and intravascular tumor thrombus, were identified as independent risk factors for RFS, with VETC-positive HCC showing significantly shorter RFS.
Conclusions:
- 3D fractal analysis of Gd-EOB-DTPA-MRI is a promising non-invasive method for predicting the VETC pattern in HCC.
- The combination of fractal parameters with clinical and radiological features enhances the prediction accuracy of the VETC pattern.
- This approach can effectively identify high-risk HCC patients, aiding in personalized treatment strategies and improved patient outcomes.
Abstract:
This study aims to evaluate the potential role of 3-dimensional (3D) fractal analysis derived from Gd-EOB-DTPA-MRI in predicting vessels encapsulating tumor clusters (VETC) pattern in patients with hepatocellular carcinoma (HCC). This retrospective study included 212 HCC patients who underwent preoperative Gd-EOB-DTPA-MRI between January 2018 and August 2024. Fractal dimension (FD) and lacunarity from arterial and hepatobiliary phases were extracted using box-counting method. Variables associated with VETC pattern were analyzed using univariate and multivariate logistic regression. Model performance was assessed using area under the curve (AUC). Recurrence-free survival (RFS) was analyzed using Kaplan-Meier methods. FD and lacunarity from arterial and hepatobiliary phases were higher in VETC-positive HCC than in VETC-negative HCC (P < 0.05). The AUC of the fractal feature model was 0.76 (0.70,0.82). Multivariate analysis identified AFP, capsule, and intratumoral necrosis as independent predictors of VETC pattern. Combining these with fractal parameters yielded a hybrid model with an AUC of 0.80 (0.75,0.86). Cox regression identified AST/ALT, intravascular tumor thrombus, and VETC pattern as risk factors for RFS, with significantly shorter RFS in VETC-positive HCC (P < 0.05). MRI-based 3D fractal parameters show significant correlation with VETC pattern. When combined with clinical radiological features, FD and lacunarity can effectively predict VETC pattern and identify high-risk HCC patients.
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