A model based on MRI radiomics features for prediction of microvascular invasion in hepatocellular carcinoma_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

QU Chengming ¹ , LI Kun ¹ , CAI Ping ² , XIE Qiao ² , YAN Xiaochu ³ , CHEN Shihan ¹ , LI Changfeng ¹ , XIA Feng ¹ , ZHANG Leida ¹ , FENG Kai ¹ ,  MA Kuansheng ¹

1. Department of Hepatobiliary Surgery, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing 400038, P. R. China;
2. Department of Radiology, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing 400038, P. R. China;
3. Department of Pathology, The First Affiliated Hospital of Army Medical University (Third Military Medical University), Chongqing 400038, P. R. China;

Corresponding author：

MA Kuansheng, Email: makuansheng@vip.sina.com

Keywords：

radiomics; machine learning; gadoxetate disodium; hepatocellular carcinoma; microvascular invasion

DOI：

10.7507/1007-9424.202009112

Video：

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Objective To establish a model for predicting microvascular invasion (MVI) of hepatocellular carcinoma based on magnetic resonance imaging (MRI) radiomics features.Methods The clinical and pathological datas of 190 patients with hepatocellular carcinoma who received surgical treatment in our hospital from September 2017 to May 2020 were prospectively collected. The patients were randomly divided into training group (n=158) and test group (n=32) with a ratio of 5∶1. Gadoxetate disodium (Gd-EOB-DTPA) -enhanced MR images of arterial phase and hepatobiliary phase were used to select radiomics features through the region of interest (ROI). The ROI included the tumor lesions and the area dilating to 2 cm from the margin of the tumor. Based on a machine learning algorithm logistic, a radiomics model for predicting MVI of hepatocellular carcinoma was established in the training group, and the model was evaluated in the test group.Results Seven radiomics features were obtained. The area under the receiver operating characteristic curve (AUC) of the training group and the test group were 0.830 [95%CI (0.669, 0.811)] and 0.734 [95%CI (0.600, 0.936)], respectively.Conclusion The model based on MRI radiomics features seems to be a promising approach for predicting the microvascular invasion of hepatocellular carcinoma, which is of clinical significance for the management of hepatocellular carcinoma treatment.

Citation： QU Chengming, LI Kun, CAI Ping, XIE Qiao, YAN Xiaochu, CHEN Shihan, LI Changfeng, XIA Feng, ZHANG Leida, FENG Kai, MA Kuansheng. A model based on MRI radiomics features for prediction of microvascular invasion in hepatocellular carcinoma. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(2): 175-180. doi: 10.7507/1007-9424.202009112 Copy

1.	Lu LC, Cheng AL, Poon RT. Recent advances in the prevention of hepatocellular carcinoma recurrence. Semin Liver Dis, 2014, 34(4): 427-434.
2.	朱海涛, 孙诚谊. 肝硬化对乙肝相关性肝癌根治术预后的影响. 中国现代医学杂志, 2018, 28(2): 57-60.
3.	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): 1133-1144.
4.	白石磊, 杨平华, 李俊, 等. 微血管侵犯对小肝细胞癌和大肝细胞癌术后远期预后的影响. 中华肝脏外科手术学电子杂志, 2017, 6(3): 207-211.
5.	段婷, 黄子星, 宋彬. 磁共振评价肝细胞癌微血管侵犯的研究进展. 中国普外基础与临床杂志, 2017, 24(7): 880-884.
6.	张小峰, 汪宇, 王文超, 等. 微血管侵犯在肝癌外科治疗中的临床意义. 肝胆外科杂志, 2017, 25(1): 7-10.
7.	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): 373-386.
8.	朱凯, 许乙凯. 肝脏特异性对比剂钆塞酸二钠预测肝癌微血管浸润的研究进展. 中华放射学杂志, 2019, 53(12): 1037-1039.
9.	胡文墨, 杨华瑜, 毛一雷. 基于人工智能的影像组学在肝脏疾病中的应用. 中华普通外科杂志, 2019, 34(7): 646-648.
10.	马风玲, 姚旭峰, 黄钢. 基于影像组学的肝癌研究进展. 医学影像学杂志, 2019, 29(5): 861-864.
11.	吴孟超, 汤钊猷, 刘彤华, 等. 原发性肝癌规范化病理诊断指南(2015年版). 临床肝胆病杂志, 2015, 31(6): 833-839.
12.	郭翌, 周世崇, 余锦华, 等. 影像组学的前沿研究与未来挑战. 肿瘤影像学, 2017, 26(2): 81-90.
13.	Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C, et al. Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int, 2019, 13(5): 546-559.
14.	Peng J, Zhang J, Zhang Q, et al. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol, 2018, 24(3): 121-127.
15.	Pinker K, Chin J, Melsaether AN, et al. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology, 2018, 287(3): 732-747.
16.	吴佩琪, 刘再毅, 何兰, 等. 影像组学与大数据结合的研究现状. 中华放射学杂志, 2017, 51(7): 554-558.
17.	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): 4648-4659.
18.	Zeng MS, Ye HY, Guo L, et al. Gd-EOB-DTPA-enhanced magnetic resonance imaging for focal liver lesions in Chinese patients: a multicenter, open-label, phase Ⅲ study. Hepatobiliary Pancreat Dis Int, 2013, 12(6): 607-616.
19.	马宽生, 蒋家云. 钆塞酸二钠增强MRI检查在精准肝脏外科中的应用. 中华消化外科杂志, 2017, 16(2): 124-129.
20.	Roayaie S, Blume IN, Thung SN, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology, 2009, 137(3): 850-855.
21.	Hu HT, Shen SL, Wang Z, et al. Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY), 2018, 43(12): 3324-3330.
22.	Ma 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.
23.	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. Front Oncol, 2020, 10: 353.

1. Lu LC, Cheng AL, Poon RT. Recent advances in the prevention of hepatocellular carcinoma recurrence. Semin Liver Dis, 2014, 34(4): 427-434.
2. 朱海涛, 孙诚谊. 肝硬化对乙肝相关性肝癌根治术预后的影响. 中国现代医学杂志, 2018, 28(2): 57-60.
3. 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): 1133-1144.
4. 白石磊, 杨平华, 李俊, 等. 微血管侵犯对小肝细胞癌和大肝细胞癌术后远期预后的影响. 中华肝脏外科手术学电子杂志, 2017, 6(3): 207-211.
5. 段婷, 黄子星, 宋彬. 磁共振评价肝细胞癌微血管侵犯的研究进展. 中国普外基础与临床杂志, 2017, 24(7): 880-884.
6. 张小峰, 汪宇, 王文超, 等. 微血管侵犯在肝癌外科治疗中的临床意义. 肝胆外科杂志, 2017, 25(1): 7-10.
7. 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): 373-386.
8. 朱凯, 许乙凯. 肝脏特异性对比剂钆塞酸二钠预测肝癌微血管浸润的研究进展. 中华放射学杂志, 2019, 53(12): 1037-1039.
9. 胡文墨, 杨华瑜, 毛一雷. 基于人工智能的影像组学在肝脏疾病中的应用. 中华普通外科杂志, 2019, 34(7): 646-648.
10. 马风玲, 姚旭峰, 黄钢. 基于影像组学的肝癌研究进展. 医学影像学杂志, 2019, 29(5): 861-864.
11. 吴孟超, 汤钊猷, 刘彤华, 等. 原发性肝癌规范化病理诊断指南(2015年版). 临床肝胆病杂志, 2015, 31(6): 833-839.
12. 郭翌, 周世崇, 余锦华, 等. 影像组学的前沿研究与未来挑战. 肿瘤影像学, 2017, 26(2): 81-90.
13. Wakabayashi T, Ouhmich F, Gonzalez-Cabrera C, et al. Radiomics in hepatocellular carcinoma: a quantitative review. Hepatol Int, 2019, 13(5): 546-559.
14. Peng J, Zhang J, Zhang Q, et al. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol, 2018, 24(3): 121-127.
15. Pinker K, Chin J, Melsaether AN, et al. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology, 2018, 287(3): 732-747.
16. 吴佩琪, 刘再毅, 何兰, 等. 影像组学与大数据结合的研究现状. 中华放射学杂志, 2017, 51(7): 554-558.
17. 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): 4648-4659.
18. Zeng MS, Ye HY, Guo L, et al. Gd-EOB-DTPA-enhanced magnetic resonance imaging for focal liver lesions in Chinese patients: a multicenter, open-label, phase Ⅲ study. Hepatobiliary Pancreat Dis Int, 2013, 12(6): 607-616.
19. 马宽生, 蒋家云. 钆塞酸二钠增强MRI检查在精准肝脏外科中的应用. 中华消化外科杂志, 2017, 16(2): 124-129.
20. Roayaie S, Blume IN, Thung SN, et al. A system of classifying microvascular invasion to predict outcome after resection in patients with hepatocellular carcinoma. Gastroenterology, 2009, 137(3): 850-855.
21. Hu HT, Shen SL, Wang Z, et al. Peritumoral tissue on preoperative imaging reveals microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY), 2018, 43(12): 3324-3330.
22. Ma 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.
23. 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. Front Oncol, 2020, 10: 353.

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

A model based on MRI radiomics features for prediction of microvascular invasion in hepatocellular carcinoma

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