Jia Lulu, Li Zeyan, Huang Gang, Jiang Hanchen, Xu Hao, Zhao Jianxin, Li Jinkui, Lei Junqiang
The First Clinical Medical College of Lanzhou University, Lanzhou, China.
Jinan University & University of Birmingham Joint Institution, Jinan University, Jinan, China.
Insights Imaging. 2025 Aug 28;16(1):186. doi: 10.1186/s13244-025-02063-w.
To develop a CT-based deep learning model for predicting the macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) and to compare its diagnostic performance with machine learning models.
We retrospectively collected contrast-enhanced CT data from patients diagnosed with HCC via histopathological examination between January 2019 and August 2023. These patients were recruited from two medical centers. All analyses were performed using two-dimensional regions of interest. We developed a novel deep learning network based on ResNet-50, named ResNet-ViT Contrastive Learning (RVCL). The RVCL model was compared against baseline deep learning models and machine learning models. Additionally, we developed a multimodal prediction model by integrating deep learning models with clinical parameters. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC).
A total of 368 patients (mean age, 56 ± 10; 285 [77%] male) from two institutions were retrospectively enrolled. Our RVCL model demonstrated superior diagnostic performance in predicting MTM (AUC = 0.93) on the external test set compared to the five baseline deep learning models (AUCs range 0.46-0.72, all p < 0.05) and the three machine learning models (AUCs range 0.49-0.60, all p < 0.05). However, integrating the clinical biomarker Alpha-Fetoprotein (AFP) into the RVCL model did not significant improvement in diagnostic performance (internal test data set: AUC 0.99 vs 0.95 [p = 0.08]; external test data set: AUC 0.98 vs 0.93 [p = 0.05]).
The deep learning model based on contrast-enhanced CT can accurately predict the MTM subtype in HCC patients, offering a smart tool for clinical decision-making.
The RVCL model introduces a transformative approach to the non-invasive diagnosis MTM subtype of HCC by harmonizing convolutional neural networks and vision transformers within a unified architecture.
The RVCL model can accurately predict the MTM subtype. Deep learning outperforms machine learning for predicting MTM subtype. RVCL boosts accuracy and guides personalized therapy.
开发一种基于CT的深度学习模型，用于预测肝细胞癌（HCC）的大小梁-大块型（MTM）亚型，并将其诊断性能与机器学习模型进行比较。
我们回顾性收集了2019年1月至2023年8月期间经组织病理学检查确诊为HCC的患者的增强CT数据。这些患者来自两个医疗中心。所有分析均使用二维感兴趣区域进行。我们基于ResNet-50开发了一种新型深度学习网络，名为ResNet-ViT对比学习（RVCL）。将RVCL模型与基线深度学习模型和机器学习模型进行比较。此外，我们通过将深度学习模型与临床参数相结合，开发了一种多模态预测模型。使用受试者操作特征曲线下面积（AUC）评估模型性能。
两个机构共回顾性纳入368例患者（平均年龄56±10岁；285例[77%]为男性）。我们的RVCL模型在外部测试集上预测MTM方面表现出优于五个基线深度学习模型（AUC范围为0.46-0.72，所有p<0.05）和三个机器学习模型（AUC范围为0.49-0.60，所有p<0.05）的诊断性能。然而，将临床生物标志物甲胎蛋白（AFP）纳入RVCL模型并未显著提高诊断性能（内部测试数据集：AUC为0.99对0.95[p=0.08]；外部测试数据集：AUC为0.98对0.93[p=0.05]）。
基于增强CT的深度学习模型能够准确预测HCC患者的MTM亚型，为临床决策提供了一个智能工具。
RVCL模型通过在统一架构中协调卷积神经网络和视觉变换器，为HCC的MTM亚型无创诊断引入了一种变革性方法。
RVCL模型能够准确预测MTM亚型。在预测MTM亚型方面，深度学习优于机器学习。RVCL提高了准确性并指导个性化治疗。
