Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 10:11:605296.
doi: 10.3389/fonc.2021.605296. eCollection 2021.

Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

Yuyan Chen  1 , Zelong Liu  2 , Yunxian Mo  3 , Bin Li  4 , Qian Zhou  4 , Sui Peng  4   5 , Shaoqiang Li  1 , Ming Kuang  1   2
Affiliations

Prediction of Post-hepatectomy Liver Failure in Patients With Hepatocellular Carcinoma Based on Radiomics Using Gd-EOB-DTPA-Enhanced MRI: The Liver Failure Model

Yuyan Chen et al. Front Oncol. .

Abstract

Objectives: Preoperative prediction of post-hepatectomy liver failure (PHLF) in patients with hepatocellular carcinoma (HCC) is significant for developing appropriate treatment strategies. We aimed to establish a radiomics-based clinical model for preoperative prediction of PHLF in HCC patients using gadolinium-ethoxybenzyl-diethylenetriamine (Gd-EOB-DTPA)-enhanced magnetic resonance imaging (MRI). Methods: A total of 144 HCC patients from two medical centers were included, with 111 patients as the training cohort and 33 patients as the test cohort, respectively. Radiomics features and clinical variables were selected to construct a radiomics model and a clinical model, respectively. A combined logistic regression model, the liver failure (LF) model that incorporated the developed radiomics signature and clinical risk factors was then constructed. The performance of these models was evaluated and compared by plotting the receiver operating characteristic (ROC) curve and calculating the area under the curve (AUC) with 95% confidence interval (CI). Results: The radiomics model showed a higher AUC than the clinical model in the training cohort and the test cohort for predicting PHLF in HCC patients. Moreover, the LF model had the highest AUCs in both cohorts [0.956 (95% CI: 0.955-0.962) and 0.844 (95% CI: 0.833-0.886), respectively], compared with the radiomics model and the clinical model. Conclusions: We evaluated quantitative radiomics features from MRI images and presented an externally validated radiomics-based clinical model, the LF model for the prediction of PHLF in HCC patients, which could assist clinicians in making treatment strategies before surgery.

Keywords: hepatocellular carcinoma; magnetic resonance imaging; post-hepatectomy liver failure; prediction model; radiomics.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The radiomics workflow. (A) Regions of interests (ROIs) were delineated in the non-tumor area; (B) Radiomics features were extracted from ROIs; (C) The selection of radiomics features; (D) The evaluation of the performance of prediction models.
Figure 2
Figure 2
Heatmaps of correlations among radiomics features. (A) Heatmap depicting correlation coefficients matrix of 1,044 radiomics features in the training cohort. (B) Heatmap depicting correlation coefficients matrix of 24 selected radiomics features in the training cohort.
Figure 3
Figure 3
Radiomics features selection. (A) The determination of 24 radiomics features; (B) The ranks of the 24 selected features.
Figure 4
Figure 4
Comparison of ROC curves between the clinical model, radiomics model, and the LF model in the training (A) and test (B) cohorts.
See this image and copyright information in PMC

References

    1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. (2018) 68:394–424. 10.3322/caac.21492 - DOI - PubMed
    1. Mai RY, Ye JZ, Long ZR, Shi XM, Bai T, Chen J, et al. Preoperative aspartate aminotransferase-to-platelet-ratio index as a predictor of posthepatectomy liver failure for resectable hepatocellular carcinoma. Cancer Manag Res. (2019) 11:1401–14. 10.2147/CMAR.S186114 - DOI - PMC - PubMed
    1. Prodeau M, Drumez E, Duhamel A, Vibert E, Farges O, Lassailly G, et al. An ordinal model to predict the risk of symptomatic liver failure in patients with cirrhosis undergoing hepatectomy. J Hepatol. (2019) 71:920–9. 10.1016/j.jhep.2019.06.003 - DOI - PubMed
    1. Tomimaru Y, Eguchi H, Gotoh K, Kawamoto K, Wada H, Asaoka T, et al. Platelet count is more useful for predicting posthepatectomy liver failure at surgery for hepatocellular carcinoma than indocyanine green clearance test. J Surg Oncol. (2016) 113:565–9. 10.1002/jso.24166 - DOI - PubMed
    1. Dasari BVM, Hodson J, Roberts KJ, Sutcliffe RP, Marudanayagam R, Mirza DF, et al. Developing and validating a pre-operative risk score to predict post-hepatectomy liver failure. HPB (Oxford). (2019) 21:539–46. 10.1016/j.hpb.2018.09.011 - DOI - PubMed

LinkOut - more resources