Background: Microvascular invasion (MVI) is a critical prognostic factor in hepatocellular carcinoma (HCC), but preoperative three-class prediction remains challenging. Radiomics and clinical biomarkers may enable more accurate and individualized assessment.Aim: The aim of this study was to develop and validate a Transformer-based deep learning framework that integrates radiomic and clinical features for direct three-class MVI classification in HCC patients.Methods: This retrospective study included 437 patients with pathologically confirmed hepatocellular carcinoma (HCC) and microvascular invasion (MVI) status from two campuses of a single institution. Patients from Hospital A (n= 305) were randomly divided into training and internal test cohorts, while patients from Hospital B (n= 132) were used as an independent external validation cohort. Radiomic features were extracted from preoperative Gd-BOPTA-enhanced MRI, and clinical laboratory data were collected. A two-stage feature selection strategy, combining univariate statistical testing and recursive feature elimination, was applied. A Transformer-based model was built to classify three MVI categories (M0, M1, M2), and its performance was evaluated in both the internal test cohort and the external validation cohort. Results were compared with those from traditional machine learning models, including Random Forest, Logistic Regression, XGBoost, and LightGBM.Results: On the internal test set (n= 76, Hospital A), the model achieved an accuracy of 0.733 (95% CI: 0.64–0.83), a weighted F1-score of 0.733, and a macro-average AUC of 0.880 (95% CI: 0.807–0.953). The sensitivity and specificity for M1 were 0.56 (95% CI: 0.31–0.78) and 0.86 (95% CI: 0.74–0.94), respectively; for high-risk M2 cases, the sensitivity was 0.73 (95% CI: 0.64–0.81) and the specificity was 0.91 (95% CI: 0.85–0.96). On the external validation set (n= 132, Hospital B), performance remained stable with an accuracy of 0.758, a weighted F1-score of 0.768, and a macro-average AUC of 0.886 (95% CI: 0.833–0.940).Conclusions: This Transformer-based model enables accurate and objective three-class MVI prediction using multi-modal features, supporting individualized surgical planning and improved clinical outcomes. In particular, the ability to preoperatively identify high-risk M2 patients may inform surgical margin design, guide adjuvant therapy strategies, and influence liver transplantation eligibility.
背景：微血管侵犯（MVI）是肝细胞癌（HCC）的关键预后因素，但其术前三级分类预测仍具挑战性。影像组学与临床生物标志物可能实现更准确和个体化的评估。 目的：本研究旨在开发并验证一种基于Transformer的深度学习框架，该框架整合影像组学特征与临床特征，用于对HCC患者进行直接的三级MVI分类。 方法：这项回顾性研究纳入了来自同一机构两个院区的437例经病理证实肝细胞癌（HCC）且具有明确微血管侵犯（MVI）状态的患者。来自A医院的患者（n=305）被随机分为训练队列和内部测试队列，而来自B医院的患者（n=132）作为独立的外部验证队列。从术前钆贝葡胺（Gd-BOPTA）增强磁共振成像（MRI）中提取影像组学特征，并收集临床实验室数据。采用结合单变量统计检验和递归特征消除的两阶段特征选择策略。构建了一个基于Transformer的模型，用于对三种MVI类别（M0、M1、M2）进行分类，并在内部测试队列和外部验证队列中评估其性能。结果与包括随机森林、逻辑回归、XGBoost和LightGBM在内的传统机器学习模型进行了比较。 结果：在内部测试集（n=76，A医院）上，该模型的准确率为0.733（95% CI：0.64–0.83），加权F1分数为0.733，宏观平均AUC为0.880（95% CI：0.807–0.953）。对于M1分类，其敏感性和特异性分别为0.56（95% CI：0.31–0.78）和0.86（95% CI：0.74–0.94）；对于高风险M2病例，敏感性为0.73（95% CI：0.64–0.81），特异性为0.91（95% CI：0.85–0.96）。在外部验证集（n=132，B医院）上，性能保持稳定，准确率为0.758，加权F1分数为0.768，宏观平均AUC为0.886（95% CI：0.833–0.940）。 结论：该基于Transformer的模型能够利用多模态特征实现准确、客观的三级MVI预测，有助于支持个体化手术规划并改善临床结局。特别是，其术前识别高风险M2患者的能力，可为手术切缘设计、辅助治疗策略制定以及肝移植资格评估提供参考。
肿瘤(癌症)患者之家