What question did this study set out to answer?

To develop a DCE-MRI-based model for noninvasive identification of tertiary lymphoid structures in HCC.

February 2, 2026Journal of Magnetic Resonance Imaging0 citations

Intratumoral Spatial Heterogeneity at Dynamic Contrast‐Enhanced MRI for Assessing Tertiary Lymphoid Structures in Hepatocellular Carcinoma

Key Points

To develop a DCE-MRI-based model for noninvasive identification of tertiary lymphoid structures in HCC.
Conducted a retrospective analysis with 334 patients with pathologically confirmed HCC.
Utilized k-means clustering to identify tumor habitats from DCE-MRI enhancement patterns.
Developed five identification models including a hybrid model integrating clinical, radiomics, and habitat features.
Evaluated model performance using area under the receiver operating characteristic curve (AUC).
Identified four distinct tumor habitats based on dynamic enhancement patterns.
The hybrid model achieved the highest AUC of 0.83 in the training set and 0.84 in the test set.
The hybrid model significantly outperformed other models in both training and test datasets.
Statistical analysis showed significant differences in model performance, particularly the hybrid model.

Abstract

ABSTRACT Background Tertiary lymphoid structures (TLS) are associated with enhanced antitumor immune activity in hepatocellular carcinoma (HCC). Dynamic contrast‐enhanced (DCE)‐MRI‐based habitat imaging may enable noninvasive TLS identification. Purpose To develop and validate a DCE‐MRI‐based habitat model to noninvasively identify TLS status in HCC. Study Type Retrospective. Subjects Three hundred and thirty‐four patients (mean age, 53.59 years ±11.38; male = 296; training set:test set = 200:134) with pathologically confirmed HCC. Field Strength/Sequence 1.5‐T/3.0‐T, contrast‐enhanced three‐dimensional gradient‐recalled‐echo T1‐weighted sequence. Assessment Tumor habitats were identified from DCE‐MRI via k‐means clustering of voxel‐wise enhancement patterns. Habitat‐derived features and radiomics features were extracted. Five identification models were developed: clinical model, radiomics model, habitat model, clinical‐radiomics model, and clinical‐radiomics‐habitat (hybrid) model. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Statistical Tests Receiver operating characteristic (ROC) curve analysis, area under the precision‐recall curve (AUPRC), DeLong test, decision curve analysis, Spiegelhalter's Z test. Significance level: p < 0.05 (two‐sided). Results Four habitats with distinct dynamic enhancement patterns were identified. The hybrid model demonstrated the highest discrimination in the training and test sets (AUC = 0.83 95% CI: 0.78, 0.89 and 0.84 95% CI: 0.78, 0.91). It significantly outperformed the clinical‐radiomics model (AUC = 0.78 95% CI: 0.72, 0.84 and 0.77 95% CI: 0.69, 0.85), radiomics model (AUC = 0.76 95% CI: 0.70, 0.83 and 0.75 95% CI: 0.67, 0.83), and clinical model (AUC = 0.70 95% CI: 0.63, 0.78 and 0.68 95% CI: 0.58, 0.77) in both sets. Compared to the habitat model (AUC = 0.78 and 0.80), the hybrid model showed significantly better training performance but comparable test performance ( p = 0.09). Conclusion A hybrid model combining clinical, radiomics, and habitat‐derived features was developed for identifying TLS status. Level of Evidence 3. Technical Efficacy Stage 2.

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