4.5 Article

Development and validation of a deep learning model for survival prognosis of transcatheter arterial chemoembolization in patients with intermediate-stage hepatocellular carcinoma

期刊

EUROPEAN JOURNAL OF RADIOLOGY
卷 156, 期 -, 页码 -

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.ejrad.2022.110527

关键词

Hepatocellular carcinoma; Convolutional neural network; Computed tomography; Prognosis; Machine learning

资金

  1. Natural Science Foundation of China
  2. Foundation of Liaoning Province Education Administration
  3. [82001904]
  4. [LJKR0278]

向作者/读者索取更多资源

Deep learning-based EfficientNetV2 model can evaluate the time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naive patients with intermediate-stage hepatocellular carcinoma (HCC). Patients with lower scores on the model have better prognosis in TACE treatment.
Purpose: We aimed to develop a deep learning-based approach to evaluate both time-to-progression (TTP) and overall survival (OS) prognosis of transcatheter arterial chemoembolization (TACE) in treatment-naive patients with intermediate-stage hepatocellular carcinoma (HCC) and compare the approach's performance with those of radiomics and clinical models.Methods: EfficientNetV2 was used to build a prognosis model for treatment-naive patients with HCC. Data of 414 intermediate-stage HCC patients from one participant center were collected to construct the training and vali-dation datasets (70%:30%) for TTP prognosis, while data of 129 intermediate-stage HCC patients from another participant center were collected as the test dataset for both TTP and OS prognosis. Three radiomics and three clinical models were then constructed for comparison.Results: Patients with EfficientNetV2-based model score < 0.5 had better TTP than those with higher scores (hazard ratio [HR]: 0.32, 95%CI: 0.22-0.46, P < 0.0001; HR: 0.28, 95%CI: 0.20-0.41, P < 0.0001; and HR: 0.55, 95%CI: 0.36-0.88, P = 0.005 in the training, validation, and test datasets, respectively). Patients with model score < 0.5 had better OS (38.8 months vs 20.9 months, HR: 0.58, 95%CI: 0.37-0.90, P = 0.008). Compared with the radiomics (intra-tumoral and peri-tumoral) and three clinical models, the EfficientNetV2-based model showed better survival prognosis for TACE (P < 0.05) in the test dataset.Conclusions: The EfficientNetV2-based model enables assessment of both TTP and OS prognosis of TACE in treatment-naive, intermediate-stage HCC. Patients with lower scores will benefit from TACE. The model can potentially be used by clinicians to improve decision making regarding TACE treatment choices.

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