Share：
Share this content in WeChat
X
[Chinese][PDF]4302
• Review •
Research progress of radiomics and deep learning in predicting microvascular invasion of hepatocellular carcinoma
LI Jie  HU Guangchao  JIANG Xingyue  ZOU Linxuan  WANG Beizhong  LI Naixuan 

Cite this article as: LI J, HU G C, JIANG X Y, et al. Research progress of radiomics and deep learning in predicting microvascular invasion of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(3): 184-188. DOI:10.12015/issn.1674-8034.2023.03.034.


[Abstract] Microvascular invasion (MVI) is considered to initiate intrahepatic metastasis and postoperative recurrence of hepatocellular carcinoma (HCC). Radiomics and deep learning (DL) can identify delicate imaging features from routinely used radiological images that are invisible to the naked eye and has been increasingly adopted to predict MVI status. In the past, few studies focused the impact of tumor size. At present, control of tumor size (2.0-5.0 cm in diameter) becomes a hot topic in the development of MVI of HCC. This review mainly analyzes the research development of radiomics and DL in MVI of HCC from the three sections of CT, MRI and PET, mainly including the similarities and differences of previous studies, the characteristics, advantages and disadvantages of the three imaging methods. Finally, for the common problems, the limitations, improvement measures and future direction of radiomics and DL are summarized. The article aims to draw the attention of readers to HCC (especially early HCC), enhancing the awareness of early diagnosis and treatment of HCC among radiologists and clinicians, providing a comprehensive comparison for researchers, so that more patients can benefit from clinical diagnosis and treatment as soon as possible and improving the quality and happiness index of life.
[Keywords] hepatocellular carcinoma；microvascular invasion；radiomics；deep learning；magnetic resonance imaging

LI Jie1   HU Guangchao2   JIANG Xingyue1   ZOU Linxuan1   WANG Beizhong1   LI Naixuan3*  

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

2 School of Medical Imaging, Binzhou Medical University, Yantai 264000, China

3 Department of Vascular Interventional Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai 264000, China

Corresponding author: Li NX, E-mail: xuannaili@163.com

Conflicts of interest   None.

Received  2022-10-12
Accepted  2023-03-07
DOI: 10.12015/issn.1674-8034.2023.03.034
Cite this article as: LI J, HU G C, JIANG X Y, et al. Research progress of radiomics and deep learning in predicting microvascular invasion of hepatocellular carcinoma[J]. Chin J Magn Reson Imaging, 2023, 14(3): 184-188. DOI:10.12015/issn.1674-8034.2023.03.034.

[1]
MARRERO J A, WELLING T. Modern diagnosis and management of hepatocellular carcinoma[J]. Clin Liver Dis, 2009, 13(2): 233-247. DOI: 10.1016/j.cld.2009.02.007.
[2]
XU X D, CHEN J H, WANG F R, et al. Recurrence of hepatocellular carcinoma after laparoscopic hepatectomy: risk factors and treatment strategies[J]. J Laparoendosc Adv Surg Tech A, 2017, 27(7): 676-684. DOI: 10.1089/lap.2016.0541.
[3]
XU X F, XING H, HAN J, et al. Risk factors, patterns, and outcomes of late recurrence after liver resection for hepatocellular carcinoma: a multicenter study from China[J]. JAMA Surg, 2019, 154(3): 209-217. DOI: 10.1001/jamasurg.2018.4334.
[4]
LIANG X, SHI S, GAO T. Preoperative gadoxetic acid-enhanced MRI predicts aggressive pathological features in LI-RADS category 5 hepatocellular carcinoma[J]. Clin Radiol, 2022, 77(9): 708-716. DOI: 10.1016/j.crad.2022.05.018.
[5]
RENZULLI M, BROCCHI S, CUCCHETTI A, et al. Can Current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma?[J]. Radiology, 2016, 279(2): 432-442. DOI: 10.1148/radiol.2015150998.
[6]
ERSTAD D J, TANABE K K. Prognostic and therapeutic implications of microvascular invasion in hepatocellular carcinoma[J]. Ann Surg Oncol, 2019, 26(5): 1474-1493. DOI: 10.1245/s10434-019-07227-9.
[7]
YAO W J, YANG S, GE Y Q, et al. Computed tomography radiomics-based prediction of microvascular invasion in hepatocellular carcinoma[J/OL]. Front Med (Lausanne), 2022, 9: 819670 [2022-12-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8987588. DOI: 10.3389/fmed.2022.819670.
[8]
ZHANG W L, YANG R M, LIANG F R, et al. Prediction of microvascular invasion in hepatocellular carcinoma with a multi-disciplinary team-like radiomics fusion model on dynamic contrast-enhanced computed tomography[J/OL]. Front Oncol, 2021, 11: 660629 [2022-12-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8008108. DOI: 10.3389/fonc.2021.660629.
[9]
MA X H, WEI J W, GU D S, et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT[J]. Eur Radiol, 2019, 29(7): 3595-3605. DOI: 10.1007/s00330-018-5985-y.
[10]
SHINDOH J, ANDREOU A, ALOIA T A, et al. Microvascular invasion does not predict long-term survival in hepatocellular carcinoma up to 2 cm: reappraisal of the staging system for solitary tumors[J]. Ann Surg Oncol, 2013, 20(4): 1223-1229. DOI: 10.1245/s10434-012-2739-y.
[11]
MIN J H, LEE M W, PARK H S, et al. Interobserver variability and diagnostic performance of gadoxetic acid-enhanced MRI for predicting microvascular invasion in hepatocellular carcinoma[J]. Radiology, 2020, 297(3): 573-581. DOI: 10.1148/radiol.2020201940.
[12]
HWANG S, LEE Y J, KIM K H, et al. The impact of tumor size on long-term survival outcomes after resection of solitary hepatocellular carcinoma: single-institution experience with 2558 patients[J]. J Gastrointest Surg, 2015, 19(7): 1281-1290. DOI: 10.1007/s11605-015-2849-5.
[13]
RUNGSAKULKIJ N, MINGPHRUEDHI S, SURAGUL W, et al. Platelet-to-lymphocyte ratio and large tumor size predict microvascular invasion after resection for hepatocellular carcinoma[J]. Asian Pac J Cancer Prev, 2018, 19(12): 3435-3441. DOI: 10.31557/APJCP.2018.19.12.3435.
[14]
WEI H, JIANG H Y, LIU X J, et al. Can LI-RADS imaging features at gadoxetic acid-enhanced MRI predict aggressive features on pathology of single hepatocellular carcinoma?[J/OL]. Eur J Radiol, 2020, 132: 109312 [2023-03-03]. https://www.sciencedirect.com/science/article/pii/S0720048X20305015?via%3Dihub. DOI: 10.1016/j.ejrad.2020.109312.
[15]
MENG X P, TANG T Y, DING Z M, et al. Preoperative microvascular invasion prediction to assist in surgical plan for single hepatocellular carcinoma: better together with radiomics[J]. Ann Surg Oncol, 2022, 29(5): 2960-2970. DOI: 10.1245/s10434-022-11346-1.
[16]
RENZULLI M, MOTTOLA M, COPPOLA F, et al. Automatically extracted machine learning features from preoperative CT to early predict microvascular invasion in HCC: the role of the zone of transition (ZOT)[J/OL]. Cancers, 2022, 14(7): 1816 [2022-12-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8997857. DOI: 10.3390/cancers14071816.
[17]
KIM J, MIN J H, KIM S K, et al. Detection of hepatocellular carcinoma in contrast-enhanced magnetic resonance imaging using deep learning classifier: a multi-center retrospective study[J/OL]. Sci Rep, 2020, 10(1): 9458 [2023-03-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289813/. DOI: 10.1038/s41598-020-65875-4.
[18]
PENG J, KANG S, NING Z Y, et al. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging[J]. Eur Radiol, 2020, 30(1): 413-424. DOI: 10.1007/s00330-019-06318-1.
[19]
MCBEE M P, AWAN O A, COLUCCI A T, et al. Deep learning in radiology[J]. Acad Radiol, 2018, 25(11): 1472-1480. DOI: 10.1016/j.acra.2018.02.018.
[20]
PARK H J, PARK B, LEE S S. Radiomics and deep learning: hepatic applications[J]. Korean J Radiol, 2020, 21(4): 387-401. DOI: 10.3348/kjr.2019.0752.
[21]
YANG Y H, ZHOU Y, ZHOU C, et al. Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma[J]. Eur J Surg Oncol, 2022, 48(5): 1068-1077. DOI: 10.1016/j.ejso.2021.11.120.
[22]
LEE H S, LEE H, HONG H, et al. Classification of focal liver lesions in CT images using convolutional neural networks with lesion information augmented patches and synthetic data augmentation[J]. Med Phys, 2021, 48(9): 5029-5046. DOI: 10.1002/mp.15118.
[23]
STOLLMAYER R, BUDAI B K, TÓTH A, et al. Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging[J]. World J Gastroenterol, 2021, 27(35): 5978-5988. DOI: 10.3748/wjg.v27.i35.5978.
[24]
TAKENAGA T, HANAOKA S, NOMURA Y, et al. Multichannel three-dimensional fully convolutional residual network-based focal liver lesion detection and classification in Gd-EOB-DTPA-enhanced MRI[J]. Int J Comput Assist Radiol Surg, 2021, 16(9): 1527-1536. DOI: 10.1007/s11548-021-02416-y.
[25]
YANG L, GU D S, WEI J W, et al. A radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma[J]. Liver Cancer, 2019, 8(5): 373-386. DOI: 10.1159/000494099.
[26]
CHONG H H, YANG L, SHENG R F, et al. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤5 cm[J]. Eur Radiol, 2021, 31(7): 4824-4838. DOI: 10.1007/s00330-020-07601-2.
[27]
FENG S T, JIA Y M, LIAO B, et al. Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI[J]. Eur Radiol, 2019, 29(9): 4648-4659. DOI: 10.1007/s00330-018-5935-8.
[28]
JIANG Y Q, CAO S E, CAO S L, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. J Cancer Res Clin Oncol, 2021, 147(3): 821-833. DOI: 10.1007/s00432-020-03366-9.
[29]
PENG J, ZHANG J, ZHANG Q F, et al. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma[J]. Diagn Interv Radiol, 2018, 24(3): 121-127. DOI: 10.5152/dir.2018.17467.
[30]
LI Z H, YE J Z, CHEN J, et al. The impact of anatomical resection for hepatocellular carcinoma with microvascular invasion on early tumor recurrence[J]. Chin J Hepatobiliary Surg, 2018, 24(1): 18-22. DOI: 10.3760/cma.j.issn.1007-8118.2018.01.005.
[31]
CHENG Y, JIA W D, XING S G, et al. Clinical efficacy of anatomic liver resection in the treatment of hepatocellular carcinoma with microvascular invasion[J]. Chin J Dig Surg, 2017, 16(2): 144-150. DOI: 10.3760/cma.j.issn.1673-9752.2017.02.008.
[32]
KIERANS A S, KANG S K, ROSENKRANTZ A B. The diagnostic performance of dynamic contrast-enhanced MR imaging for detection of small hepatocellular carcinoma measuring up to 2 cm: a meta-analysis[J]. Radiology, 2016, 278(1): 82-94. DOI: 10.1148/radiol.2015150177.
[33]
LI J F, WANG J M, LEI L P, et al. The diagnostic performance of gadoxetic acid disodium-enhanced magnetic resonance imaging and contrast-enhanced multi-detector computed tomography in detecting hepatocellular carcinoma: a meta-analysis of eight prospective studies[J]. Eur Radiol, 2019, 29(12): 6519-6528. DOI: 10.1007/s00330-019-06294-6.
[34]
RHEE H, AN C, KIM H Y, et al. Hepatocellular carcinoma with irregular rim-like arterial phase hyperenhancement: more aggressive pathologic features[J]. Liver Cancer, 2019, 8(1): 24-40. DOI: 10.1159/000488540.
[35]
YANG Y, FAN W J, GU T, et al. Radiomic features of multi-ROI and multi-phase MRI for the prediction of microvascular invasion in solitary hepatocellular carcinoma[J/OL]. Front Oncol, 2021, 11: 756216 [2023-02-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529277/. DOI: 10.3389/fonc.2021.756216.
[36]
TIAN Y Q, HUA H, PENG Q 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]. J Magn Reson Imaging, 2022, 56(5): 1459-1472. DOI: 10.1002/jmri.28157.
[37]
LEE S, KIM S H, LEE J E, et al. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma[J]. J Hepatol, 2017, 67(3): 526-534. DOI: 10.1016/j.jhep.2017.04.024.
[38]
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[J]. Quant Imaging Med Surg, 2019, 9(9): 1503-1515. DOI: 10.21037/qims.2019.09.07.
[39]
DONG Y, ZHOU L, XIA W, et al. Preoperative prediction of microvascular invasion in hepatocellular carcinoma: initial application of a radiomic algorithm based on grayscale ultrasound images[J/OL]. Front Oncol, 2020, 10: 353 [2023-03-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096379/. DOI: 10.3389/fonc.2020.00353.
[40]
NEBBIA G, ZHANG Q, AREFAN D, et al. Pre-operative microvascular invasion prediction using multi-parametric liver MRI radiomics[J]. J Digit Imaging, 2020, 33(6): 1376-1386. DOI: 10.1007/s10278-020-00353-x.
[41]
HU F, ZHANG Y H, LI M, et al. Preoperative prediction of microvascular invasion risk grades in hepatocellular carcinoma based on tumor and peritumor dual-region radiomics signatures[J/OL]. Front Oncol, 2022, 12: 853336 [2023-02-28]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8981726/. DOI: 10.3389/fonc.2022.853336.
[42]
CHU T J, ZHAO C, ZHANG J, et al. Application of a convolutional neural network for multitask learning to simultaneously predict microvascular invasion and vessels that encapsulate tumor clusters in hepatocellular carcinoma[J]. Ann Surg Oncol, 2022, 29(11): 6774-6783. DOI: 10.1245/s10434-022-12000-6.
[43]
RENNE S L, WOO H Y, ALLEGRA S, et al. Vessels encapsulating tumor clusters (VETC) is a powerful predictor of aggressive hepatocellular carcinoma[J]. Hepatology, 2020, 71(1): 183-195. DOI: 10.1002/hep.30814.
[44]
FANG J H, ZHOU H C, ZHANG C, et al. A novel vascular pattern promotes metastasis of hepatocellular carcinoma in an epithelial-mesenchymal transition-independent manner[J]. Hepatology, 2015, 62(2): 452-465. DOI: 10.1002/hep.27760.
[45]
FANG J H, XU L, SHANG L R, et al. Vessels that encapsulate tumor clusters (VETC) pattern is a predictor of sorafenib benefit in patients with hepatocellular carcinoma[J]. Hepatology, 2019, 70(3): 824-839. DOI: 10.1002/hep.30366.
[46]
YANG D W, JIA X B, XIAO Y J, et al. Noninvasive evaluation of the pathologic grade of hepatocellular carcinoma using MCF-3DCNN: a pilot study[J]. Biomed Res Int, 2019, 2019: 9783106 [2023-03-03]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6512077/. DOI: 10.1155/2019/9783106.
[47]
HUANG J C, LIU J Y, HU J H, et al. The microvascular invasion predictive value of radiomics based on MRI enhancement in the patients with hepatocellular carcinoma[J]. Radiol Pract, 2022, 37(10): 1243-1248. DOI: 10.13609/j.cnki.1000-0313.2022.10.010.
[48]
LI Y C, ZHANG Y, FANG Q, et al. Radiomics analysis of[18F]FDG PET/CT for microvascular invasion and prognosis prediction in very-early- and early-stage hepatocellular carcinoma[J]. Eur J Nucl Med Mol Imaging, 2021, 48(8): 2599-2614. DOI: 10.1007/s00259-020-05119-9.
[49]
SHI H Z, DUAN Y, SHI J, et al. Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: a comparison of quantitative metabolic parameters and MRI[J/OL]. Front Physiol, 2022, 13: 928969 [2022-12-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9412047. DOI: 10.3389/fphys.2022.928969.
[50]
FILIPPI L, SCHILLACI O, BAGNI O. Recent advances in PET probes for hepatocellular carcinoma characterization[J]. Expert Rev Med Devices, 2019, 16(5): 341-350. DOI: 10.1080/17434440.2019.1608817.

PREV The application of left atrial strain derived from cardiac magnetic resonance in cardiac diseases
NEXT Research progress of amide proton transfer imaging in rectal neoplasms
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn