Dataset Information

Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.

ABSTRACT:

Background

The Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study of the Liver (EASL) criteria are widely used for diagnosing hepatocellular carcinoma (HCC). Radiomics allows further quantitative tumor heterogeneity profiling. This study aimed to compare the diagnostic accuracies of the version 2018 (v2018) EASL, LI-RADS criteria and radiomics models for HCC in high-risk patients.

Methods

Ethical approval by the institutional review board and informed consent were obtained for this study. From July 2015 to September 2018, consecutive high-risk patients were enrolled in our tertiary care hospital and underwent gadoxetic acid-enhanced magnetic resonance (MR) imaging and subsequent hepatic surgery. We constructed a multi-sequence-based three-dimensional whole-tumor radiomics signature by least absolute shrinkage and selection operator model and multivariate logistic regression analysis. The diagnostic accuracies of the radiomics signature was validated in an independent cohort and compared with the EASL and LI-RADS criteria reviewed by two independent radiologists.

Results

Two hundred twenty-nine pathologically confirmed nodules (173 HCCs, mean size: 5.74?±?3.17?cm) in 211 patients were included. Among them, 201 patients (95%) were infected with hepatitis B virus (HBV). The sensitivity and specificity were 73 and 71% for the radiomics signature, 91 and 71% for the EASL criteria, and 86 and 82% for the LI-RADS criteria, respectively. The areas under the receiver operating characteristic curves (AUCs) of the radiomics signature (0.810), LI-RADS (0.841) and EASL criteria (0.811) were comparable.

Conclusions

In HBV-predominant high-risk patients, the multi-sequence-based MR radiomics signature, v2018 EASL and LI-RADS criteria demonstrated comparable overall accuracies for HCC.

SUBMITTER: Jiang H

PROVIDER: S-EPMC6896342 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.

Jiang Hanyu H Liu Xijiao X Chen Jie J Wei Yi Y Lee Jeong Min JM Cao Likun L Wu Yuanan Y Duan Ting T Li Xin X Ma Ling L Song Bin B

Cancer imaging : the official publication of the International Cancer Imaging Society 20191205 1

<h4>Background</h4>The Liver Imaging Reporting and Data System (LI-RADS) and European Association for the Study of the Liver (EASL) criteria are widely used for diagnosing hepatocellular carcinoma (HCC). Radiomics allows further quantitative tumor heterogeneity profiling. This study aimed to compare the diagnostic accuracies of the version 2018 (v2018) EASL, LI-RADS criteria and radiomics models for HCC in high-risk patients.<h4>Methods</h4>Ethical approval by the institutional review board and ...[more]

PMID: 31806050

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Project description:ObjectiveTo evaluate the performance of the 2018 Korean Liver Cancer Association-National Cancer Center (KLCA-NCC) Practice Guidelines (hereafter, PG) for the diagnosis of hepatocellular carcinoma (HCC) using gadoxetic acid-enhanced MRI, compared to the Liver Imaging-Reporting and Data System (LI-RADS) version 2018 (hereafter, v2018).Materials and methodsFrom January 2013 to October 2015, treatment-naïve hepatic lesions (≥ 1 cm) on gadoxetic acid-enhanced MRI in consecutive patients with chronic hepatitis B or cirrhosis were retrospectively evaluated. For each lesion, three radiologists independently analyzed the imaging features and classified the lesions into categories according to the 2018 KLCA-NCC PG and LI-RADS v2018. The imaging features and categories were determined by consensus. Generalized estimating equation (GEE) models were used to compare the per-lesion diagnostic performance of the 2018 KLCA-NCC PG and LI-RADS v2018 using the consensus data.ResultsIn total, 422 lesions (234 HCCs, 45 non-HCC malignancies, and 143 benign lesions) from 387 patients (79% male; mean age, 59 years) were included. In all lesions, the definite HCC (2018 KLCA-NCC PG) had a higher sensitivity and lower specificity than LR-5 (LI-RADS v2018) (87.2% [204/234] vs. 80.8% [189/234], p < 0.001; 86.2% [162/188] vs. 91.0% [171/188], p = 0.002). However, in lesions of size ≥ 2 cm, the definite HCC had a higher sensitivity than the LR-5 (86.8% [164/189] vs. 82.0 (155/189), p = 0.002) without a reduction in the specificity (80.0% [48/60] vs. 83.3% [50/60], p = 0.15). In all lesions, the sensitivity and specificity of the definite/probable HCC (2018 KLCA-NCC PG) and LR-5/4 did not differ significantly (89.7% [210/234] vs. 91.5% [214/234], p = 0.204; 83.5% [157/188] vs. 79.3% [149/188], p = 0.071).ConclusionFor the diagnosis of HCC of size ≥ 2 cm, the definite HCC (2018 KLCA-NCC PG) had a higher sensitivity than LR-5, without a reduction in specificity. The definite/probable HCC (2018 KLCA-NCC PG) had a similar sensitivity and specificity to that those of the LR-5/4.

Dataset Information

Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.

Background

Methods

Results

Conclusions

Publications

Man or machine? Prospective comparison of the version 2018 EASL, LI-RADS criteria and a radiomics model to diagnose hepatocellular carcinoma.

Similar Datasets

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