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

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2.	朱海濤, 孫誠誼. 肝硬化對乙肝相關性肝癌根治術預后的影響. 中國現代醫學雜志, 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, 楊平華, 李俊, 等. 微血管侵犯對小肝細胞癌和大肝細胞癌術后遠期預后的影響. 中華肝臟外科手術學電子雜志, 2017, 6(3): 207-211.
5.	段婷, 黃子星, 宋彬. 磁共振評價肝細胞癌微血管侵犯的研究進展. 中國普外基礎與臨床雜志, 2017, 24(7): 880-884.
6.	張小峰, 汪宇, 王文超, 等. 微血管侵犯在肝癌外科治療中的臨床意義. 肝膽外科雜志, 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.	朱凱, 許乙凱. 肝臟特異性對比劑釓塞酸二鈉預測肝癌微血管浸潤的研究進展. 中華放射學雜志, 2019, 53(12): 1037-1039.
9.	胡文墨, 楊華瑜, 毛一雷. 基于人工智能的影像組學在肝臟疾病中的應用. 中華普通外科雜志, 2019, 34(7): 646-648.
10.	馬風玲, 姚旭峰, 黃鋼. 基于影像組學的肝癌研究進展. 醫學影像學雜志, 2019, 29(5): 861-864.
11.	吳孟超, 湯釗猷, 劉彤華, 等. 原發性肝癌規范化病理診斷指南(2015年版). 臨床肝膽病雜志, 2015, 31(6): 833-839.
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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.	吳佩琪, 劉再毅, 何蘭, 等. 影像組學與大數據結合的研究現狀. 中華放射學雜志, 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.	馬寬生, 蔣家云. 釓塞酸二鈉增強MRI檢查在精準肝臟外科中的應用. 中華消化外科雜志, 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. 朱海濤, 孫誠誼. 肝硬化對乙肝相關性肝癌根治術預后的影響. 中國現代醫學雜志, 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, 楊平華, 李俊, 等. 微血管侵犯對小肝細胞癌和大肝細胞癌術后遠期預后的影響. 中華肝臟外科手術學電子雜志, 2017, 6(3): 207-211.
5. 段婷, 黃子星, 宋彬. 磁共振評價肝細胞癌微血管侵犯的研究進展. 中國普外基礎與臨床雜志, 2017, 24(7): 880-884.
6. 張小峰, 汪宇, 王文超, 等. 微血管侵犯在肝癌外科治療中的臨床意義. 肝膽外科雜志, 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. 朱凱, 許乙凱. 肝臟特異性對比劑釓塞酸二鈉預測肝癌微血管浸潤的研究進展. 中華放射學雜志, 2019, 53(12): 1037-1039.
9. 胡文墨, 楊華瑜, 毛一雷. 基于人工智能的影像組學在肝臟疾病中的應用. 中華普通外科雜志, 2019, 34(7): 646-648.
10. 馬風玲, 姚旭峰, 黃鋼. 基于影像組學的肝癌研究進展. 醫學影像學雜志, 2019, 29(5): 861-864.
11. 吳孟超, 湯釗猷, 劉彤華, 等. 原發性肝癌規范化病理診斷指南(2015年版). 臨床肝膽病雜志, 2015, 31(6): 833-839.
12. 郭翌, 周世崇, 余錦華, 等. 影像組學的前沿研究與未來挑戰. 腫瘤影像學, 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. 吳佩琪, 劉再毅, 何蘭, 等. 影像組學與大數據結合的研究現狀. 中華放射學雜志, 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. 馬寬生, 蔣家云. 釓塞酸二鈉增強MRI檢查在精準肝臟外科中的應用. 中華消化外科雜志, 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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