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Advance in multicenter research of hepatocellular carcinoma based on radiomics
YANG Haoran  MA Mimi  CAO Xinshan 

Cite this article as: Yang HR, Ma MM, Cao XS. Advance in multicenter research of hepatocellular carcinoma based on radiomics[J]. Chin J Magn Reson Imaging, 2021, 12(8): 101-103. DOI:10.12015/issn.1674-8034.2021.08.023.


[Abstract] Radiomics is an interdisciplinary discipline of medical and engineering course that conducts quantitative analysis of medical images with high throughput characteristics. Hepatocellular carcinoma (HCC) is the most common primary liver cancer. Due to its temporal and spatial heterogeneity, it is suitable for radiomics analysis. In order to improve the repeatability and generalization ability of radiomics, large-scale multi center research has become a hot topic in recent years. This review mainly summarizes the latest progress in multicenter research of hepatocellular carcinoma based on radiomics.
[Keywords] radiomics；hepatocellular carcinoma；multicenter study；prognosis of postoperative；image standardization；magnetic resonance imaging

YANG Haoran   MA Mimi   CAO Xinshan*  

Department of Radiology, Affiliated Hospital of Binzhou Medical College, Binzhou 256603, China

Cao XS, E-mail: byfycxs@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS This work was part of Natural Science Foundation of Shandong Province (No. Y2008C177).
Received  2021-04-13
Accepted  2021-05-13
DOI: 10.12015/issn.1674-8034.2021.08.023
Cite this article as: Yang HR, Ma MM, Cao XS. Advance in multicenter research of hepatocellular carcinoma based on radiomics[J]. Chin J Magn Reson Imaging, 2021, 12(8): 101-103. DOI:10.12015/issn.1674-8034.2021.08.023.

1
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
2
Qiu G, Jin Z, Chen X, et al. Interpretation of guidelines for the diagnosis and treatment of primary liver cancer (2019 edition) in China[J]. Global Health & Med, 2020, 2(5): 306-311. DOI: 10.35772/ghm.2020.01051.
3
Forner A, Rodríguez De Lope C, Reig M, et al. Early diagnosis of primary liver cancer: imaging versus genetics[J]. Rev Esp Enferm Dig, 2008, 100(7): 423-429. DOI: 10.4321/s1130-01082008000700008.
4
Hennedige T, Venkatesh SK. Advances in computed tomography and magnetic resonance imaging of hepatocellular carcinoma[J]. World J Gastroenterol, 2016, 22(1): 205-220. DOI: 10.3748/wjg.v22.i1.205.
5
Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology[J]. Nat Rev Cancer, 2018, 18(8): 500-510. DOI: 10.1038/s41568-018-0016-5.
6
Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer?[J]. Nat Rev Cancer, 2012, 12(5): 323-34. DOI: 10.1038/nrc3261.
7
Giger ML. Machine learning in medical imaging[J]. J Am Coll Radiol, 2018, 15 (3Pt B): 512-520. DOI: 10.1016/j.jacr.2017.12.028.
8
Mcbee MP, Awan OA, Colucci AT, et al. Deep learning in radiology[J]. Acad Radiol, 2018, 25(11): 1472-1480. DOI: 10.1016/j.acra.2018.02.018.
9
Chartrand G, Cheng PM, Vorontsov E, et al. Deep learning: A primer for radiologists[J]. Radiographics, 2017, 37(7): 2113-2131. DOI: 10.1148/rg.2017170077.
10
Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis[J]. Eur Radiol Exp, 2018, 2(1): 36. DOI: 10.1186/s41747-018-0068-z.
11
Zwanenburg A. Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis[J]. Eur J Nucl Med Mol Imaging, 2019, 46(13): 2638-2655. DOI: 10.1007/s00259-019-04391-8.
12
Jochems A, Deist TM, Van Soest J, et al. Distributed learning: Developing a predictive model based on data from multiple hospitals without data leaving the hospital-A real life proof of concept[J]. Radiother Oncol, 2016, 121(3): 459-467. DOI: 10.1016/j.radonc.2016.10.002.
13
Bogowicz M, Leijenaar RTH, Tanadini-Lang S, et al. Post-radiochemotherapy PET radiomics in head and neck cancer-The influence of radiomics implementation on the reproducibility of local control tumor models[J]. Radiother Oncol, 2017, 125(3): 385-391. DOI: 10.1016/j.radonc.2017.10.023.
14
Da-Ano R, Visvikis D, Hatt M. Harmonization strategies for multicenter radiomics investigations[J]. Phys Med Biol, 2020, 65(24): 24TR02. DOI: 10.1088/1361-6560/aba798.
15
Liu P, Zhang J, Liang R, et al. The application of radiomics in intrahepatic cholangiocarcinoma[J]. Chin J Magn Reson Imaging, 2021, 12(3): 109-111, 115. DOI: 10.12015/issn.1674-8034.2021.03.027.
16
Wang YB, Wang ST, Xie L, et al. Diffusion weighted imaging radiomics model for differential diagnosis of hepatocellular carcinoma and hepatic haemangioma[J]. J Clin Radiol, 2020, 39(3): 481-486. DOI: 10.13437/j.cnki.jcr.2020.03.013.
17
Gu D, Xie Y, Wei J, et al. MRI-based radiomics signature: A potential biomarker for identifying glypican 3-positive hepatocellular carcinoma[J]. J Magn Reson Imaging, 2020, 2020: e27199. DOI: 10.1002/jmri.27199.
18
Hectors SJ, Lewis S, Besa C, et al. MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma[J]. Eur Radiol, 2020, 30(7): 3759-3769. DOI: 10.1007/s00330-020-06675-2.
19
Xu LL, Shu J, Yang CM. Evaluation of pathological grading in hepatocellular carcinoma by radiomics signature of MRI[J]. J Clin Radiol, 2020, 39(8): 1520-1523. DOI: 10.13437/j.cnki.jcr.2020.08.013.
20
Hui TCH, Chuah TK, Low HM, et al. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: a radiomics study[J]. Clin Radiol, 2018, 73(12): 1056. DOI: 10.1016/j.crad.2018.07.109.
21
Wu M, Tan H, Gao F, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature[J]. Eur Radiol, 2019, 29 (6): 2802-2811. DOI: 10.1007/s00330-018-5787-2.
22
Duan YY, Zhou KP, Bian J, et al. Predicting microvascular invasion of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. [J]. Chin J Magn Reson Imaging, 2020, 11(3): 195-200. DOI: 10.12015/issn.1674-8034.2020.03.007.
23
Jiang YQ, Cao SE, Cao S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. J Cancer Res Clin Oncol, 2020, 147(3): 821-833. DOI: 10.1007/s00432-020-03366-9.
24
Li L, Yang RD, Wang Z, et al. Prognosis of primary liver cancer patients based on machine learning method[J]. Chin Digital Med, 2019, 14(3): 34-37. DOI: 10.3969/j.issn.1673-7571.2019.03.009.
25
Akai H, Yasaka K, Kunimatsu A, et al. Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest[J]. Diagn Interv Imaging, 2018, 99(10): 643-651. DOI: 10.1016/j.diii.2018.05.008.
26
Zhang Z, Chen J, Jiang H, et al. Gadoxetic acid-enhanced MRI radiomics signature: prediction of clinical outcome in hepatocellular carcinoma after surgical resection[J]. Ann Transl Med, 2020, 8(14): 870. DOI: 10.21037/atm-20-3041.
27
Huang X, Long L, Wei J, et al. Radiomics for diagnosis of dual-phenotype hepatocellular carcinoma using Gd-EOB-DTPA-enhanced MRI and patient prognosis[J]. J Cancer Res Clin Oncol, 2019, 145(12): 2995-3003. DOI: 10.1007/s00432-019-03062-3.
28
Sun Y, Bai H, Xia W, et al. Predicting the outcome of transcatheter arterial embolization therapy for unresectable hepatocellular carcinoma based on radiomics of preoperative multiparameter MRI[J]. J Magn Reson Imaging, 2020, 52(4): 1083-1090. DOI: 10.1002/jmri.27143.
29
Song W, Yu X, Guo D, et al. MRI-based radiomics: Associations with the recurrence-free survival of patients with hepatocellular carcinoma treated with conventional transcatheter arterial chemoembolization[J]. J Magn Reson Imaging, 2020, 52(2): 461-473. DOI: 10.1002/jmri.26977.
30
Mokrane FZ, Lu L, Vavasseur A, et al. Radiomics machine-learning signature for diagnosis of hepatocellular carcinoma in cirrhotic patients with indeterminate liver nodules[J]. Eur Radiol, 2020, 30(1): 558-570. DOI: 10.1007/s00330-019-06347-w.
31
Zhang X, Ruan S, Xiao W, et al. Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study[J]. Clin Transl Med, 2020, 10(2). DOI: 10.1002/ctm2.111.
32
Ji GW, Zhu FP, Xu Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study[J]. EBioMedicine, 2019, 50: 156-165. DOI: 10.1016/j.ebiom.2019.10.057.
33
Wang XH, Long LH, Cui Y, et al. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma[J]. Br J Cancer, 2020, 122(7): 978-985. DOI: 10.1038/s41416-019-0706-0.
34
Meng XP, Wang YC, Ju S, et al. Radiomics analysis on multiphase contrast-enhanced CT: A survival prediction tool in patients with hepatocellular carcinoma undergoing transarterial chemoembolization[J]. Front Oncol, 2020, 10: 1196. DOI: 10.3389/fonc.2020.01196.
35
Jin Z, Chen L, Zhong B, et al. Machine-learning analysis of contrast-enhanced computed tomography radiomics predicts patients with hepatocellular carcinoma who are unsuitable for initial transarterial chemoembolization monotherapy: A multicenter study[J]. Transl Oncol, 2021, 14(4): 101034. DOI: 10.1016/j.tranon.2021.101034.

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