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February 2, 2026British Journal of Hospital MedicineOpen Access

Survival Prediction and Treatment Decisions in Hepatocellular Carcinoma: A Deep Learning-Based Radiomics Approach

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Authors

XWXiaoqin WeiNorth Sichuan Medical UniversityJXJun XiaoNorth Sichuan Medical UniversityYLYing LiuFirst Affiliated Hospital of Chengdu Medical College

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Implication

Demonstrates the effectiveness of machine learning models to predict survival in HCC patients receiving different treatments, suggesting improved decision-making for clinicians.

Key Points

  • The aim is to assess the effectiveness of integating deep learning radiomics with clinical data to predict survival in hepatocellular carcinoma (HCC) patients.
  • Included pathologically confirmed HCC patients who underwent hepatectomy or TACE from January 2013 to December 2022.
  • Utilized deep learning algorithms ResNet50, ResNet18, and DenseNet121 with CT images to predict overall survival time.
  • Developed combined survival models integrating clinical factors and deep learning features for both treatments.
  • Assessed model discrimination using AUC of ROC curves and evaluated survival risk with Kaplan-Meier analysis.
  • ResNet50 achieved the highest AUC of 0.866 in the training cohort and 0.793 in the testing cohort.
  • Combined models showed superior discriminative performance for predicting survival after hepatectomy and TACE treatment.
  • C-index for the combined hepatectomy model was 0.836 in training and 0.861 in testing cohorts; C-index for combined TACE model was 0.840 in training and 0.834 in testing cohorts.

Cite This Study

Wei et al. (2026) studied this question.

synapsesocial.com/papers/6980fe68c1c9540dea81076dhttps://doi.org/10.31083/bjhm50380
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