Hybrid imaging-clinical model for predicting microvascular invasion in hepatocellular carcinoma using deep learning-derived features from CT.

Journal: Physical and engineering sciences in medicine
Published Date: Jan 19, 2026

Abstract

The role of adipose tissue in predicting microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains unclear. This study proposes a method that integrates deep learning and machine learning techniques to investigate the role of adipose tissue in identifying MVI status in HCC patients. We collected enhanced Computed Tomography images from 517 HCC patients across two independent centers, dividing them into a training set, validation set, and test set. The model was constructed using adipose and tumor deep learning features along with clinical features, and the features were input into a classifier for prediction. The model performance was evaluated using the area under the curve(AUC), decision curve analysis, scatter plots, and box plots. Furthermore, we compared the model's performance with that of three radiologists. After incorporating the adipose tissue modality, the venous-phase AUC reached 0.866 (95% CI 0.803-0.920), while the arterial-phase AUC was 0.864 (95% CI 0.792-0.920). The inclusion of the adipose tissue modality provided significant value for clinical diagnosis, which was further validated through visualization analysis. Using predicted labels for grouping, it shows that the overall survival of the high-risk group was significantly lower than that of the low-risk group. Comparative analysis showed that the predictive performance of the model surpassed that of radiologists. Univariate analysis identified the adipose region as a risk factor for predicting MVI status. We developed a hybrid multimodal model that performed comparably to radiologists' assessments. The inclusion of the adipose tissue modality enhanced the accuracy of MVI diagnosis.

Authors

  • Shidi Miao
    School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
  • Mingxuan Wang
    College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning Province 110819, China.
  • Qi Dong
    Institute of Biomechanics and Rehabilitation Engineering, School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P.R.China;Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, P.R.China;Key Laboratory of Nerve Function Information and Rehabilitation Engineering, Shanghai 200093, P.R.China.
  • Qifan Xuan
    School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
  • Le Liu
    Department of Ultrasound, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China.
  • Mengzhuo Sun
    School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.
  • Yuyang Jiang
    The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, P. R. China.
  • Yuxin Jiang
    Department of Ultrasound, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Beijing, China.
  • Ruitao Wang
    Department of Internal Medicine, Harbin Medical University Cancer Hospital, Harbin Medical University, Harbin, China.
  • Qiujun Wang
    Department of General Practice, The Second Affiliated Hospital, Harbin Medical University, Harbin, China.
  • Zengyao Liu
    Department of Interventional Medicine, The First Affiliated Hospital, Harbin Medical University, Harbin, China.
  • Xuemei Ding
    Department of Surgery, Affiliated Hospital of Qingdao University, Qingdao, China.
  • Haipeng Jin
    School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, China.

Keywords

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