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Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

  • Hepatobiliary
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Abdominal Radiology Aims and scope Submit manuscript

Abstract

Purpose

To assess the methodological quality and to evaluate the predictive performance of radiomics studies for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

Methods

Publications between 2017 and 2021 on radiomic MVI prediction in HCC based on CT, MR, ultrasound, and PET/CT were included. The risk of bias was assessed using the prediction model risk of bias assessment tool (PROBAST). Methodological quality was assessed through the radiomics quality score (RQS). Fourteen studies classified as TRIPOD Type 2a or above were used for meta-analysis using random-effects model. Further analyses were performed to investigate the technical factors influencing the predictive performance of radiomics models.

Results

Twenty-three studies including 4947 patients were included. The risk of bias was mainly related to analysis domain. The RQS reached an average of (37.7 ± 11.4)% with main methodological insufficiencies of scientific study design, external validation, and open science. The pooled areas under the receiver operating curve (AUC) were 0.85 (95% CI 0.82–0.89), 0.87 (95% CI 0.83–0.92), and 0.74 (95% CI 0.67–0.80), respectively, for CT, MR, and ultrasound radiomics models. The pooled AUC of ultrasound radiomics model was significantly lower than that of CT (p = 0.002) and MR (p < 0.001). Portal venous phase for CT and hepatobiliary phase for MR were superior to other imaging sequences for radiomic MVI prediction. Segmentation of both tumor and peritumor regions showed better performance than tumor region.

Conclusion

Radiomics models show promising prediction performance for predicting MVI in HCC. However, improvements in standardization of methodology are required for feasibility confirmation and clinical translation.

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Abbreviations

MVI:

Microvascular invasion

HCC:

Hepatocellular carcinoma

PROBAST:

Prediction model risk of bias assessment tool

RQS:

Radiomics quality score

AUC:

Area under the receiver operating characteristic

PRISMA:

The preferred reporting items for systematic reviews and meta-analyses

TP:

True positive

TN:

True negative

FP:

False positive

FN:

False negative

CT:

Computed tomography

MRI:

Magnetic resonance imaging

US:

Ultrasound

PET:

Positron emission tomography

TRIPOD:

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis

N/l:

Number of training patterns of the smallest class/the number of selected features

AP:

Arterial phase

PVP:

Portal venous phase

HBP:

Hepatobiliary phase

ROI:

Region of interest

ROB:

Risk of bias

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Funding

This study has received funding by National Natural Science Foundation of China (No. 81530055 and 81901768).

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Author notes

  1. Xian Zhong and Haiyi Long contributed equally to this work and should be considered as co-first authors.

Authors and Affiliations

  1. Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China

    Xian Zhong, Haiyi Long, Liya Su, Ruiying Zheng, Wei Wang, Yu Duan, Hangtong Hu, Manxia Lin & Xiaoyan Xie

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  1. Xian Zhong
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Correspondence to Manxia Lin or Xiaoyan Xie.

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Zhong, X., Long, H., Su, L. et al. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol 47, 2071–2088 (2022). https://doi.org/10.1007/s00261-022-03496-3

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