An Interpretable Radiomics-Based Model Using Susceptibility-Weighted Imaging for Non-Invasive Prediction of Tertiary Lymphoid Structures in Hepatocellular Carcinoma

作者：Lizhen Liu, Gao Fen, Yiman Li, Jie Cheng, Huarong Zhang, Ping Cai, Wei Chen, Xiaoming Li · 发表于：Journal of Hepatocellular Carcinoma · 年份：2025 · DOI：10.2147/jhc.s551462 · 被引用次数：7 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Cholangiocarcinoma and Gallbladder Cancer Studies

Background: Intratumoral tertiary lymphoid structures (iTLSs) are associated with favorable prognosis and immunotherapy response in hepatocellular carcinoma (HCC). This study aimed to develop an interpretable susceptibility-weighted imaging (SWI)-based radiomics model to non-invasively predict iTLSs in HCC. Materials and Methods: A retrospective cohort of 477 HCC patients undergoing preoperative SWI was used (training: 290; validation: 125; independent validation: 62). Radiomics models were constructed using five machine learning algorithms: logistic regression, random forest (RF), support vector machine, extreme gradient boosting, and K-nearest neighbors. Model performance was evaluated using the area under the ROC curve (AUC), model interpretability was examined using shapley additive explanations (SHAP), and survival analyses were performed to assess clinical relevance. Results: In the independent validation cohort, the RF algorithm was identified as the optimal classifier, with an AUC of 0.771 (95% CI: 0.641-0.883), sensitivity of 78.6%, and specificity of 67.6%. It significantly outperformed the radiological model (p = 0.046), and showed comparable performance with the hybrid model in predicting iTLSs positivity (iTLSs+) (p > 0.05). SHAP analysis showed that radiomics features (logarithm_firstorder_Minimum and exponential_glszm_ZoneEntropy) were significant predictors of iTLSs+. Kaplan-Meier analysis demonstrated improved time-to-recurrence (TTR) in the iTLSs+ predictor ...