Volume 56, Issue 5 pp. 1459-1472

Research Article

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

Yaqi Tian MD,

Yaqi Tian MD

orcid.org/0000-0002-8381-1452

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Hui Hua MD,

Hui Hua MD

Department of Thyroid Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China

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Qiqi Peng MD,

Qiqi Peng MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Zaixian Zhang MD,

Zaixian Zhang MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Xiaolin Wang MD,

Xiaolin Wang MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Junqi Han MD,

Junqi Han MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Wenjuan Ma PhD,

Corresponding Author

Wenjuan Ma PhD

[email protected]

Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

Address reprint requests to: W.M., Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China. E-mail: [email protected], or J.C., Department of Breast Imaging, Affiliated Hospital of Qingdao University, 59 Haier Road, Laoshan District, Qingdao City, Shandong Province, China. E-mail: [email protected]

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Jingjing Chen MD,

Corresponding Author

Jingjing Chen MD

[email protected]

orcid.org/0000-0002-1298-5359

Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China

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Yaqi Tian MD,

Yaqi Tian MD

orcid.org/0000-0002-8381-1452

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Hui Hua MD,

Hui Hua MD

Department of Thyroid Surgery, The Affiliated Hospital of Qingdao University, Qingdao, China

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Qiqi Peng MD,

Qiqi Peng MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Zaixian Zhang MD,

Zaixian Zhang MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Xiaolin Wang MD,

Xiaolin Wang MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Junqi Han MD,

Junqi Han MD

Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China

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Wenjuan Ma PhD,

Corresponding Author

Wenjuan Ma PhD

[email protected]

Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China

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Jingjing Chen MD,

Corresponding Author

Jingjing Chen MD

[email protected]

orcid.org/0000-0002-1298-5359

Department of Breast Imaging, The Affiliated Hospital of Qingdao University, Qingdao, China

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First published: 17 March 2022

https://doi.org/10.1002/jmri.28157

Citations: 6

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Abstract

Background

Preoperative evaluation of microvascular invasion (MVI) in small solitary hepatocellular carcinoma (HCC; maximum lesion diameter ≤ 3 cm) is important for treatment decisions.

Purpose

To apply gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI to develop and validate a nomogram for preoperative evaluation of MVI in small solitary HCC and to compare the effectiveness of radiomics evaluation models based on different volumes of interest (VOIs).

Study Type

Retrospective.

Population

A total of 196 patients include 62 MVI-positive and 134 MVI-negative patients were enrolled (training cohort, n = 105; testing cohort, n = 45; external validation cohort, n = 46).

Field Strength/Sequence

3.0 T, fat suppressed fast-spin-echo T2-weighted and Gd-EOB-DTPA-enhanced T1-weighted magnetization-prepared rapid gradient-echo sequences.

Assessment

Radiomics features were extracted on T2-weighted, arterial phase (AP), and hepatobiliary phase (HBP) images from different VOIs (VOI_intratumor and VOI_{intratumor+peritumor}) and filtered by the least absolute shrinkage selection operator (LASSO) regression. From VOI_intratumor and VOI_{intratumor+peritumor}, eight radiomics models were constructed based on three MRI sequences (T2-weighted, AP, and HBP) and fused sequences (combined of three sequences). Nomograms were constructed of a clinical-radiological (CR) model and a clinical-radiological-radiomics (CRR) model.

Statistical Tests

One-way analysis of variance, independent t-test, Chi-square test or Fisher's exact test, Wilcoxon rank-sum test, LASSO, logistic regression analysis, area under the curve (AUC), nomograms, decision curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and DeLong test.

Results

Among eight radiomics models, the fused sequences-based VOI_{intratumor+peritumor} radiomics model showed the best performance. The CRR model containing the best performance radiomics model and CR model with the AUC values were 0.934, 0.889, and 0.875, respectively. NRI and IDI analyses showed that the CRR model improved evaluation efficacy over the CR model for all three cohorts (all P-value <0.05).

Data Conclusion

The CRR model nomogram could preoperatively evaluate MVI in small solitary HCC. The radiomics model based on VOI_{intratumor+peritumor} might achieve better evaluation results.

Evidence Level

Technical Efficacy Stage

Supporting Information

References

1Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
10.3322/caac.21492
PubMed Web of Science® Google Scholar
2Rodríguez-Perálvarez M, Luong TV, Andreana L, Meyer T, Dhillon AP, Burroughs AK. A systematic review of microvascular invasion in hepatocellular carcinoma: Diagnostic and prognostic variability. Ann Surg Oncol 2013; 20(1): 325-339.
10.1245/s10434-012-2513-1
PubMed Web of Science® Google Scholar
3Rao SX, Zeng MS. Development in clinical application of hepatocyte-specific contrast agent of gadoxetic acid. Chin J Radiol 2019; 53(12): 1031-1036.
Google Scholar
4Du M, Chen L, Zhao J, et al. Microvascular invasion (MVI) is a poorer prognostic predictor for small hepatocellular carcinoma. BMC Cancer 2014; 14(1):38.
10.1186/1471-2407-14-38
PubMed Web of Science® Google Scholar
5Lin S, Ye F, Rong W, et al. Nomogram to assist in surgical plan for hepatocellular carcinoma: A prediction model for microvascular invasion. J Gastrointest Surg 2019; 23(12): 2372-2382.
10.1007/s11605-019-04140-0
PubMed Web of Science® Google Scholar
6Liu X, Jiang H, Chen J, Zhou Y, Huang Z, Song B. Gadoxetic acid disodium-enhanced magnetic resonance imaging outperformed multidetector computed tomography in diagnosing small hepatocellular carcinoma: A meta-analysis. Liver Transpl 2017; 23(12): 1505-1518.
10.1002/lt.24867
PubMed Web of Science® Google Scholar
7Ahn SY, Lee JM, Joo I, et al. Prediction of microvascular invasion of hepatocellular carcinoma using gadoxetic acid-enhanced MR and (18)F-FDG PET/CT. Abdom Imaging 2015; 40(4): 843-851.
10.1007/s00261-014-0256-0
PubMed Google Scholar
8Kim KA, Kim MJ, Jeon HM, et al. Prediction of microvascular invasion of hepatocellular carcinoma: Usefulness of peritumoral hypointensity seen on gadoxetate disodium-enhanced hepatobiliary phase images. J Magn Reson Imaging 2012; 35(3): 629-634.
10.1002/jmri.22876
CAS PubMed Web of Science® Google Scholar
9Lee S, Kim SH, Lee JE, Sinn DH, Park CK. Preoperative gadoxetic acid-enhanced MRI for predicting microvascular invasion in patients with single hepatocellular carcinoma. J Hepatol 2017; 67(3): 526-534.
10.1016/j.jhep.2017.04.024
CAS PubMed Web of Science® Google Scholar
10Hu H, Zheng Q, Huang Y, et al. A non-smooth tumor margin on preoperative imaging assesses microvascular invasion of hepatocellular carcinoma: A systematic review and meta-analysis. Sci Rep 2017; 7(1):15375.
10.1038/s41598-017-15491-6
PubMed Web of Science® Google Scholar
11Huang M, Liao B, Xu P, et al. Prediction of microvascular invasion in hepatocellular carcinoma: Preoperative Gd-EOB-DTPA-dynamic enhanced MRI and histopathological correlation. Contrast Media Mol Imaging 2018; 2018:9674565.
10.1155/2018/9674565
PubMed Web of Science® Google Scholar
12Xu P, Zeng M, Liu K, Shan Y, Xu C, Lin J. Microvascular invasion in small hepatocellular carcinoma: Is it predictable with preoperative diffusion-weighted imaging? J Gastroenterol Hepatol 2014; 29(2): 330-336.
10.1111/jgh.12358
CAS PubMed Web of Science® Google Scholar
13Wei Y, Huang Z, Tang H, et al. IVIM improves preoperative assessment of microvascular invasion in HCC. Eur Radiol 2019; 29(10): 5403-5414.
10.1007/s00330-019-06088-w
PubMed Web of Science® Google Scholar
14Rao C, Wang X, Li M, Zhou G, Gu H. Value of T1 mapping on gadoxetic acid-enhanced MRI for microvascular invasion of hepatocellular carcinoma: A retrospective study. BMC Med Imaging 2020; 20(1): 43.
10.1186/s12880-020-00433-y
PubMed Web of Science® Google Scholar
15Zhou Y, Wang X, Xu C, et al. Mass-forming intrahepatic cholangiocarcinoma: Can diffusion-weighted imaging predict microvascular invasion? J Magn Reson Imaging 2019; 50(1): 315-324.
10.1002/jmri.26566
PubMed Web of Science® Google Scholar
16Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 2017; 14(12): 749-762.
10.1038/nrclinonc.2017.141
PubMed Web of Science® Google Scholar
17Pinker K, Shitano F, Sala E, et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging 2018; 47(3): 604-620.
10.1002/jmri.25870
PubMed Web of Science® Google Scholar
18Feng 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): 4648-4659.
10.1007/s00330-018-5935-8
PubMed Web of Science® Google Scholar
19Xu 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): 1133-1144.
10.1016/j.jhep.2019.02.023
PubMed Web of Science® Google Scholar
20Zhang 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): 1503-1515.
10.21037/qims.2019.09.07
PubMed Web of Science® Google Scholar
21Lee S, Kang TW, Song KD, et al. Effect of microvascular invasion risk on early recurrence of hepatocellular carcinoma after surgery and radiofrequency ablation. Ann Surg 2021; 273(3): 564-571.
10.1097/SLA.0000000000003268
PubMed Web of Science® Google Scholar
22Yamashita Y-I, Imai K, Yusa T, et al. Microvascular invasion of single small hepatocellular carcinoma ≤3 cm: Predictors and optimal treatments. Ann Gastroenterol Surg 2018; 2(3): 197-203.
10.1002/ags3.12057
PubMed Web of Science® Google Scholar
23Wang X, Zhang Z, Zhou X, et al. Computational quantitative measures of Gd-EOB-DTPA enhanced MRI hepatobiliary phase images can predict microvascular invasion of small HCC. Eur J Radiol 2020; 133:109361.
10.1016/j.ejrad.2020.109361
PubMed Web of Science® Google Scholar
24Dong 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. Front Oncol 2020; 10:353.
10.3389/fonc.2020.00353
PubMed Web of Science® Google Scholar
25Chong HH, Yang L, Sheng RF, 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. Eur Radiol 2021; 31(7): 4824-4838.
10.1007/s00330-020-07601-2
CAS PubMed Web of Science® Google Scholar
26Lee S, Kim SH, Park CK, Kim YS, Lee WJ, Lim HK. Comparison between areas with Gd-EOB-DTPA uptake and without in hepatocellular carcinomas on Gd-EOB-DTPA-enhanced hepatobiliary-phase MR imaging: Pathological correlation. J Magn Reson Imaging 2010; 32(3): 719-725.
10.1002/jmri.22291
CAS PubMed Web of Science® Google Scholar
27Ellmann S, Schlicht M, Dietzel M, et al. Computer-aided diagnosis in multiparametric MRI of the prostate: An open-access online tool for lesion classification with high accuracy. Cancers (Basel) 2020; 12(9):2366.
10.3390/cancers12092366
Web of Science® Google Scholar
28Kurth MJ, McBride WT, McLean G, et al. Acute kidney injury risk in orthopaedic trauma patients pre and post surgery using a biomarker algorithm and clinical risk score. Sci Rep 2020; 10(1):20005.
10.1038/s41598-020-76929-y
CAS PubMed Web of Science® Google Scholar
29Uno H, Tian L, Cai T, Kohane IS, Wei LJ. A unified inference procedure for a class of measures to assess improvement in risk prediction systems with survival data. Stat Med 2013; 32(14): 2430-2442.
10.1002/sim.5647
CAS PubMed Web of Science® Google Scholar
30Shi R, Zhang T, Sun H, Hu F. Establishment of clinical prediction model based on the study of risk factors of stroke in patients with type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2020; 11:559.
10.3389/fendo.2020.00559
PubMed Web of Science® Google Scholar
31Tian YQ, Hu YB, Peng QQ, Su XH, Wang XL, Chen JJ. The value of Gd-EOB-DTPA-enhanced MRI nomogram model in predicting microvascular invasion of small solitary hepatocellular carcinoma. Chin J Magn Reson Imaging 2021; 12(10): 57-60, 65.
Google Scholar
32Kitao A, Matsui O, Yoneda N, et al. Gadoxetic acid-enhanced MR imaging for hepatocellular carcinoma: Molecular and genetic background. Eur Radiol 2020; 30(6): 3438-3447.
10.1007/s00330-020-06687-y
PubMed Web of Science® Google Scholar
33Tsuboyama T, Onishi H, Kim T, et al. Hepatocellular carcinoma: Hepatocyte-selective enhancement at gadoxetic acid-enhanced MR imaging—Correlation with expression of sinusoidal and canalicular transporters and bile accumulation. Radiology 2010; 255(3): 824-833.
10.1148/radiol.10091557
PubMed Web of Science® Google Scholar
34Surov 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.
10.1159/000511384
PubMed Web of Science® Google Scholar
35Chang WC, Chen RC, Chou CT, et al. Histological grade of hepatocellular carcinoma correlates with arterial enhancement on gadoxetic acid-enhanced and diffusion-weighted MR images. Abdom Imaging 2014; 39(6): 1202-1212.
10.1007/s00261-014-0168-z
PubMed Google Scholar
36Nebbia G, Zhang Q, Arefan D, Zhao X, Wu S. Pre-operative microvascular invasion prediction using multi-parametric liver MRI Radiomics. J Digit Imaging 2020; 33(6): 1376-1386.
10.1007/s10278-020-00353-x
PubMed Web of Science® Google Scholar
37Yang 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.
10.3389/fonc.2021.756216
PubMed Web of Science® Google Scholar
38Wei M, Gu B, Song S, et al. A novel validated recurrence stratification system based on (18)F-FDG PET/CT radiomics to guide surveillance after resection of pancreatic cancer. Front Oncol 2021; 11:650266.
10.3389/fonc.2021.650266
PubMed Web of Science® Google Scholar
39Shin J, Lim JS, Huh Y-M, et al. A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting. Sci Rep 2021; 11(1):1879.
10.1038/s41598-021-81408-z
CAS PubMed Web of Science® Google Scholar
40Kawahara D, Tang X, Lee CK, Nagata Y, Watanabe Y. Predicting the local response of metastatic brain tumor to gamma knife radiosurgery by radiomics with a machine learning method. Front Oncol 2021; 10:569461.
10.3389/fonc.2020.569461
PubMed Web of Science® Google Scholar
41Zhang 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.
10.3389/fonc.2021.633596
PubMed Web of Science® Google Scholar
42Ma X, Wei J, Gu D, et al. Preoperative radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using contrast-enhanced CT. Eur Radiol 2019; 29(7): 3595-3605.
10.1007/s00330-018-5985-y
PubMed Web of Science® Google Scholar
43Yang L, Gu D, Wei J, et al. A Radiomics nomogram for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Liver Cancer 2019; 8(5): 373-386.
10.1159/000494099
CAS PubMed Web of Science® Google Scholar

Citing Literature

Volume56, Issue5

November 2022

Pages 1459-1472

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

Abstract

Background

Purpose

Study Type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy Stage

Supporting Information

References

Citing Literature

References

Information

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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

Abstract

Background

Purpose

Study Type

Population

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

Evidence Level

Technical Efficacy Stage

Supporting Information

References

Citing Literature

References

Related

Information