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Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics

American journal of roentgenology (1976), 2018-11, Vol.211 (5), p.1026-1034 [Peer Reviewed Journal]

ISSN: 0361-803X ;EISSN: 1546-3141 ;DOI: 10.2214/AJR.18.19507 ;PMID: 30240304

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  • Title:
    Predicting Survival Using Pretreatment CT for Patients With Hepatocellular Carcinoma Treated With Transarterial Chemoembolization: Comparison of Models Using Radiomics
  • Author: Kim, Jonghoon ; Choi, Seung Joon ; Lee, Seung-Hak ; Lee, Ho Yun ; Park, Hyunjin
  • Is Part Of: American journal of roentgenology (1976), 2018-11, Vol.211 (5), p.1026-1034
  • Description: The purpose of this study was to investigate the use of radiomics features as prognostic biomarkers for predicting the survival of patients treated with transarterial chemoembolization (TACE) for hepatocellular carcinoma (HCC). We retrospectively analyzed 88 patients with HCC treated with TACE. High-dimensional quantitative feature analysis was applied to extract 116 radiomics features of pretreatment CT. A radiomics score model was constructed from these features with the use of least absolute shrinkage and selection operator Cox regression. A clinical score model was constructed from clinical variables with the use of multivariate Cox regression. A combined score model was constructed using the radiomics and clinical models. We compared the three models (the radiomics score, clinical score, and combined score models) for predicting overall survival, using Kaplan-Meier analysis and the log-rank test. The following radiomics features were selected for the radiomics score model: histogram-based features (median, kurtosis, and energy), shape-based features (spherical disproportion and surface-to-volume ratio), gray-level co-occurrence matrix (GLCM)-based features (energy, informational measure of correlation, maximum probability, contrast, and sum average), and intensity size zone matrix-based features (size zone variability). For the clinical score model, the Child-Pugh score, α-fetoprotein level, and HCC size were included. The combined score model included five radiomics features (surface area-to-volume ratio, kurtosis, median, gray-level co-occurrence matrix contrast, and size zone variability) and three clinical factors (Child-Pugh score, α-fetoprotein level, and HCC size). The combined model was a better predictor of survival (hazard ratio, 19.88; p < 0.0001) than the clinical score model or the radiomics score model. A radiomics approach combined with conventional clinical variables could be effective in predicting the survival of patients with HCC treated with TACE.
  • Publisher: United States
  • Language: English
  • Identifier: ISSN: 0361-803X
    EISSN: 1546-3141
    DOI: 10.2214/AJR.18.19507
    PMID: 30240304
  • Source: Geneva Foundation Free Medical Journals at publisher websites
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