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. 2024 Dec 18:11:2471-2480.
doi: 10.2147/JHC.S499436. eCollection 2024.

Deep Learning-Based Automatic Segmentation Combined with Radiomics to Predict Post-TACE Liver Failure in HCC Patients

Shuai Li #  1 , Kaicai Liu #  1 , Chang Rong #  1 , Xiaoming Zheng  1 , Bo Cao  2 , Wei Guo  3 , Xingwang Wu  1
Affiliations

Deep Learning-Based Automatic Segmentation Combined with Radiomics to Predict Post-TACE Liver Failure in HCC Patients

Shuai Li et al. J Hepatocell Carcinoma. .

Abstract

Objective: To develop and validate a deep learning-based automatic segmentation model and combine with radiomics to predict post-TACE liver failure (PTLF) in hepatocellular carcinoma (HCC) patients.

Methods: This was a retrospective study enrolled 210 TACE-trated HCC patients. Automatic segmentation model based on nnU-Net neural network was developed to segment medical images and assessed by the Dice similarity coefficient (DSC). The screened clinical and radiomics variables were separately used to developed clinical and radiomics predictive model, and were combined through multivariate logistic regression analysis to develop a combined predictive model. The area under the curve (AUC), calibration curve, and decision curve analysis (DCA) were applied to compare the performance of the three predictive models.

Results: The automatic segmentation model showed satisfactory segmentation performance with an average DSC of 83.05% for tumor segmentation and 92.72% for non-tumoral liver parenchyma segmentation. The international normalized ratio (INR) and albumin (ALB) was identified as clinically independent predictors for PTLF and used to develop clinical predictive model. Ten most valuable radiomics features, including 8 from non-tumoral liver parenchyma and 2 from tumor, were selected to develop radiomics predictive model and to calculate Radscore. The combined predictive model achieved the best and significantly improved predictive performance (AUC: 0.878) compared to the clinical predictive model (AUC: 0.785) and the radiomics predictive model (AUC: 0.815).

Conclusion: This reliable combined predictive model can accurately predict PTLF in HCC patients, which can be a valuable reference for doctors in making suitable treatment plan.

Keywords: TACE; deep learning; hepatocellular carcinoma; liver failure; radiomics.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
(a) The depict of study design (b) Flow chart of the radiomics study.
Figure 2
Figure 2
The network architecture of nnU-Net for non-tumoral liver parenchyma and tumor segmentation.
Figure 3
Figure 3
(a) LASSO path of radiomics. (b) Selected radiomics features and their corresponding weights.
Figure 4
Figure 4
(a) ROC curves for the three models in the validation cohort. (b) DCA for the three models in the validation cohort. (c) Calibration curve of the combined model in the training cohort. (d) Calibration curve of the combined model in the validation cohort.
Figure 5
Figure 5
Clinical-Radiomics nomogram.
See this image and copyright information in PMC

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