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Current Medical Imaging

Editor-in-Chief

ISSN (Print): 1573-4056
ISSN (Online): 1875-6603

Research Article

A Systematic Review and Meta-Analysis of MRI Radiomics for Predicting Microvascular Invasion in Patients with Hepatocellular Carcinoma

Author(s): Hai-ying Zhou, Jin-mei Cheng, Tian-wu Chen*, Xiao-ming Zhang, Jing Ou, Jin-ming Cao and Hong-jun Li*

Volume 20, 2024

Published on: 13 February, 2024

Article ID: e15734056256824 Pages: 11

DOI: 10.2174/0115734056256824231204073534

open_access

Abstract

Background: The prediction power of MRI radiomics for microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC) remains uncertain.

Objective: To investigate the prediction performance of MRI radiomics for MVI in HCC.

Methods: Original studies focusing on preoperative prediction performance of MRI radiomics for MVI in HCC, were systematically searched from databases of PubMed, Embase, Web of Science and Cochrane Library. Radiomics quality score (RQS) and risk of bias of involved studies were evaluated. Meta-analysis was carried out to demonstrate the value of MRI radiomics for MVI prediction in HCC. Influencing factors of the prediction performance of MRI radiomics were identified by subgroup analyses.

Results: 13 studies classified as type 2a or above according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis statement were eligible for this systematic review and meta-analysis. The studies achieved an average RQS of 14 (ranging from 11 to 17), accounting for 38.9% of the total points. MRI radiomics achieved a pooled sensitivity of 0.82 (95%CI: 0.78 – 0.86), specificity of 0.79 (95%CI: 0.76 – 0.83) and area under the summary receiver operator characteristic curve (AUC) of 0.88 (95%CI: 0.84 – 0.91) to predict MVI in HCC. Radiomics models combined with clinical features achieved superior performances compared to models without the combination (AUC: 0.90 vs 0.85, P < 0.05).

Conclusion: MRI radiomics has the potential for preoperative prediction of MVI in HCC. Further studies with high methodological quality should be designed to improve the reliability and reproducibility of the radiomics models for clinical application.

The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42022333822).

[1]
Omata M, Cheng AL, Kokudo N, et al. Asia–Pacific clinical practice guidelines on the management of hepatocellular carcinoma: A 2017 update. Hepatol Int 2017; 11(4): 317-70.
[http://dx.doi.org/10.1007/s12072-017-9799-9] [PMID: 28620797]
[2]
Erstad DJ, Tanabe KK. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma. Ann Surg Oncol 2019; 26(5): 1474-93.
[http://dx.doi.org/10.1245/s10434-019-07227-9] [PMID: 30788629]
[3]
Li J, Yang F, Li J, Huang ZY, Cheng Q, Zhang EL. Postoperative adjuvant therapy for hepatocellular carcinoma with microvascular invasion. World J Gastrointest Surg 2023; 15(1): 19-31.
[http://dx.doi.org/10.4240/wjgs.v15.i1.19] [PMID: 36741072]
[4]
Zheng Z, Guan R, Jianxi W, et al. Microvascular invasion in hepatocellular carcinoma: A review of its definition, clinical significance, and comprehensive management. J Oncol 2022; 2022: 1-10.
[http://dx.doi.org/10.1155/2022/9567041] [PMID: 35401743]
[5]
Rastogi A. Changing role of histopathology in the diagnosis and management of hepatocellular carcinoma. World J Gastroenterol 2018; 24(35): 4000-13.
[http://dx.doi.org/10.3748/wjg.v24.i35.4000] [PMID: 30254404]
[6]
Reginelli A, Vacca G, Segreto T, et al. Can microvascular invasion in hepatocellular carcinoma be predicted by diagnostic imaging? A critical review. Future Oncol 2018; 14(28): 2985-94.
[http://dx.doi.org/10.2217/fon-2018-0175] [PMID: 30084651]
[7]
Hong SB, Choi SH, Kim SY, et al. MRI features for predicting microvascular invasion of hepatocellular carcinoma: A systematic review and meta-analysis. Liver Cancer 2021; 10(2): 94-106.
[http://dx.doi.org/10.1159/000513704] [PMID: 33981625]
[8]
Surov A, Pech M, Omari J, et al. Diffusion-weighted imaging reflects tumor grading and microvascular invasion in hepatocellular carcinoma. Liver Cancer 2021; 10(1): 10-24.
[http://dx.doi.org/10.1159/000511384] [PMID: 33708636]
[9]
Lv K, Cao X, Du P, Fu JY, Geng DY, Zhang J. Radiomics for the detection of microvascular invasion in hepatocellular carcinoma. World J Gastroenterol 2022; 28(20): 2176-83.
[http://dx.doi.org/10.3748/wjg.v28.i20.2176] [PMID: 35721882]
[10]
Xu T, Ren L, Liao M, et al. Preoperative radiomics analysis of contrast-enhanced CT for microvascular invasion and prognosis stratification in hepatocellular carcinoma. J Hepatocell Carcinoma 2022; 9: 189-201.
[http://dx.doi.org/10.2147/JHC.S356573] [PMID: 35340666]
[11]
Zhou HY, Cheng JM, Chen TW. CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Clinics 2023; 78: 100264.
[12]
Hu F, Zhang Y, Li M, et al. Preoperative prediction of microvascular invasion risk grades in hepatocellular carcinoma based on tumor and peritumor dual-region radiomics signatures. Front Oncol 2022; 12: 853336.
[http://dx.doi.org/10.3389/fonc.2022.853336] [PMID: 35392229]
[13]
Tian Y, Hua H, Peng Q, et al. Preoperative evaluation of Gd-EOB-DTPA-Enhanced MRI radiomics-based nomogram in small solitary hepatocellular carcinoma (≤3 cm) with microvascular invasion: A two-center study. J Magn Reson Imaging 2022; 56(5): 1459-72.
[http://dx.doi.org/10.1002/jmri.28157] [PMID: 35298849]
[14]
Jiang T, He S, Yang H, et al. Multiparametric MRI-based radiomics for the prediction of microvascular invasion in hepatocellular carcinoma. Acta Radiol 2022; 2022: 2841851221080830.
[PMID: 35354318]
[15]
Li L, Su Q, Yang H. Preoperative prediction of microvascular invasion in hepatocellular carcinoma: A radiomic nomogram based on MRI. Clin Radiol 2022; 77(4): e269-79.
[http://dx.doi.org/10.1016/j.crad.2021.12.008] [PMID: 34980458]
[16]
Yang Y, Fan W, Gu T, et al. Radiomic features of multi-ROI and multi-phase MRI for the Prediction of microvascular invasion in solitary hepatocellular carcinoma. Front Oncol 2021; 11: 756216.
[http://dx.doi.org/10.3389/fonc.2021.756216] [PMID: 34692547]
[17]
Zhang Y, Lv X, Qiu J, et al. Deep learning with 3D convolutional neural network for noninvasive prediction of microvascular invasion in hepatocellular carcinoma. J Magn Reson Imaging 2021; 54(1): 134-43.
[http://dx.doi.org/10.1002/jmri.27538] [PMID: 33559293]
[18]
Zeng Q, Liu B, Xu Y, Zhou W. An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging. Phys Med Biol 2021; 66(18): 185019.
[http://dx.doi.org/10.1088/1361-6560/ac22db] [PMID: 34469880]
[19]
Song D, Wang Y, Wang W, et al. Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters. J Cancer Res Clin Oncol 2021; 147(12): 3757-67.
[http://dx.doi.org/10.1007/s00432-021-03617-3] [PMID: 33839938]
[20]
Zhang Y, Shu Z, Ye Q, et al. Preoperative Prediction of microvascular invasion in hepatocellular carcinoma via multi-parametric MRI radiomics. Front Oncol 2021; 11: 633596.
[http://dx.doi.org/10.3389/fonc.2021.633596] [PMID: 33747956]
[21]
Zhu YJ, Feng B, Wang S, et al. Model‑based three‑dimensional texture analysis of contrast‑enhanced magnetic resonance imaging as a potential tool for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Oncol Lett 2019; 18(1): 720-32.
[http://dx.doi.org/10.3892/ol.2019.10378] [PMID: 31289547]
[22]
Zhang R, Xu L, Wen X, et al. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Quant Imaging Med Surg 2019; 9(9): 1503-15.
[http://dx.doi.org/10.21037/qims.2019.09.07] [PMID: 31667137]
[23]
Feng ST, Jia Y, Liao B, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: A radiomics model using Gd-EOB-DTPA-enhanced MRI. Eur Radiol 2019; 29(9): 4648-59.
[http://dx.doi.org/10.1007/s00330-018-5935-8] [PMID: 30689032]
[24]
Yang L, Gu D, Wei J, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 2019; 8(5): 373-86.
[http://dx.doi.org/10.1159/000494099] [PMID: 31768346]
[25]
Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ 2021; 372: n71.
[http://dx.doi.org/10.1136/bmj.n71] [PMID: 33782057]
[26]
Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. BMJ 2015; 350: g7594.
[http://dx.doi.org/10.1136/bmj.g7594] [PMID: 25569120]
[27]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14(12): 749-62.
[http://dx.doi.org/10.1038/nrclinonc.2017.141] [PMID: 28975929]
[28]
Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011; 155(8): 529-36.
[http://dx.doi.org/10.7326/0003-4819-155-8-201110180-00009] [PMID: 22007046]
[29]
Xu X, Zhang HL, Liu QP, et al. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol 2019; 70(6): 1133-44.
[http://dx.doi.org/10.1016/j.jhep.2019.02.023] [PMID: 30876945]
[30]
Ni M, Zhou X, Lv Q, et al. Radiomics models for diagnosing microvascular invasion in hepatocellular carcinoma: Which model is the best model? Cancer Imaging 2019; 19(1): 60.
[http://dx.doi.org/10.1186/s40644-019-0249-x] [PMID: 31455432]
[31]
Schmid M, Gefeller O, Waldmann E, Mayr A, Hepp T. Approaches to regularized regression - A comparison between gradient boosting and the lasso. Methods Inf Med 2016; 55(5): 422-30.
[http://dx.doi.org/10.3414/ME16-01-0033] [PMID: 27626931]
[32]
Dai H, Lu M, Huang B, et al. Considerable effects of imaging sequences, feature extraction, feature selection, and classifiers on radiomics-based prediction of microvascular invasion in hepatocellular carcinoma using magnetic resonance imaging. Quant Imaging Med Surg 2021; 11(5): 1836-53.
[http://dx.doi.org/10.21037/qims-20-218] [PMID: 33936969]
[33]
Zhang J, Huang S, Xu Y, Wu J. Diagnostic accuracy of artificial intelligence based on imaging data for preoperative prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis. Front Oncol 2022; 12: 763842.
[http://dx.doi.org/10.3389/fonc.2022.763842] [PMID: 35280776]
[34]
Meng XP, Wang YC, Zhou JY, et al. Comparison of MRI and CT for the prediction of microvascular invasion in solitary hepatocellular carcinoma based on a non-radiomics and radiomics method: Which imaging modality is better? J Magn Reson Imaging 2021; 54(2): 526-36.
[http://dx.doi.org/10.1002/jmri.27575] [PMID: 33622022]
[35]
Stanzione A, Cuocolo R, Ugga L, et al. Oncologic imaging and radiomics: A walkthrough review of methodological challenges. Cancers 2022; 14(19): 4871.
[http://dx.doi.org/10.3390/cancers14194871] [PMID: 36230793]
[36]
Nie K, Xiao Y. Radiomics in clinical trials: Perspectives on standardization. Phys Med Biol 2023; 68(1): 01TR01.
[http://dx.doi.org/10.1088/1361-6560/aca388] [PMID: 36384049]
[37]
Yang Y, Zhou Y, Zhou C, Ma X. Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma. Eur J Surg Oncol 2022; 48(5): 1068-77.
[http://dx.doi.org/10.1016/j.ejso.2021.11.120] [PMID: 34862094]
[38]
Sun BY, Gu PY, Guan RY, et al. Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma. World J Surg Oncol 2022; 20(1): 189.
[http://dx.doi.org/10.1186/s12957-022-02645-8] [PMID: 35676669]
[39]
Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Reports 2022; 4(4): 100443.
[http://dx.doi.org/10.1016/j.jhepr.2022.100443] [PMID: 35243281]
[40]
Zhong J, Hu Y, Si L, et al. A systematic review of radiomics in osteosarcoma: Utilizing radiomics quality score as a tool promoting clinical translation. Eur Radiol 2021; 31(3): 1526-35.
[http://dx.doi.org/10.1007/s00330-020-07221-w] [PMID: 32876837]
[41]
Park JE, Kim D, Kim HS, et al. Quality of science and reporting of radiomics in oncologic studies: Room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 2020; 30(1): 523-36.
[http://dx.doi.org/10.1007/s00330-019-06360-z] [PMID: 31350588]

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