Application status and prospects of radiomics in diagnosis and treatment of biliary tract cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

LI Qi , JIN Zhechuan , LI Mengke ,  GENG Zhimin

Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an 710061, P. R. China;

Corresponding author：

GENG Zhimin, Email: gengzhimin@mail.xjtu.edu.cn

Keywords：

cholangiocarcinoma; radiomics; deep learning

DOI：

10.7507/1007-9424.202209099

Video：

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Biliary tract cancer is characterized by occult onset, highly malignancy and poor prognosis. Traditional medical imaging is an important tool for surgical strategies and prognostic assessment, but it can no longer meet the urgent need for accurate and individualized treatment in patients with biliary tract cancer. With the advent of the digital imaging era, the advancement of artificial intelligence technology has given a new vitality to digital imaging, and provided more possibilities for the development of medical imaging in clinical applications. The application of radiomics in the diagnosis and differential diagnosis of benign and malignant tumors of biliary tract, assessment of lymph node status, early recurrence and prognosis assessment provides new means for the diagnosis and treatment of patients with biliary tract cancer.

Citation： LI Qi, JIN Zhechuan, LI Mengke, GENG Zhimin. Application status and prospects of radiomics in diagnosis and treatment of biliary tract cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2022, 29(12): 1546-1553. doi: 10.7507/1007-9424.202209099 Copy

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4. Fiz F, Jayakody Arachchige VS, Gionso M, et al. Radiomics of Biliary Tumors: A Systematic Review of Current Evidence. Diagnostics (Basel), 2022, 12(4): 826. doi: 10.3390/diagnostics12040826.
5. Granata V, Fusco R, Belli A, et al. Conventional, functional and radiomics assessment for intrahepatic cholangiocarcinoma. Infect Agent Cancer, 2022, 17(1): 13. doi: 10.1186/s13027-022-00429-z.
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9. Ren S, Li Q, Liu S, et al. Clinical value of machine learning-based ultrasomics in preoperative differentiation between hepatocellular carcinoma and intrahepatic cholangiocarcinoma: A multicenter study. Front Oncol, 2021, 11: 749137. doi: 10.3389/fonc.2021.749137.
10. Chen Y, Lu Q, Zhang W, et al. Preoperative differentiation of combined hepatocellular-cholangiocarcinoma from hepatocellular carcinoma and intrahepatic cholangiocarcinoma: a nomogram based on ultrasonographic features and clinical indicators. Front Oncol, 2022, 12: 757774. doi: 10.3389/fonc.2022.757774.
11. Zhang J, Huang Z, Cao L, et al. Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning. Ann Transl Med, 2020, 8(4): 119. doi:10.21037/atm.2020.01.126.
12. Xu X, Mao Y, Tang Y, et al. Classification of hepatocellular carcinoma and intrahepatic cholangiocarcinoma based on radiomic analysis. Comput Math Methods Med, 2022, 2022: 5334095. doi:10.1155/2022/5334095.
13. 张加辉, 陈峰, 薛星, 等. 基于支持向量机的MRI影像组学方法鉴别不同病理分型原发性肝癌的价值. 中华放射学杂志, 2018, 52(5): 333-337.
14. Wang X, Wang S, Yin X, et al. MRI-based radiomics distinguish different pathological types of hepatocellular carcinoma. Comput Biol Med, 2022, 141: 105058. doi:10.1016/j.compbiomed.2021.105058.
15. Zhou Y, Zhou G, Zhang J, et al. DCE-MRI based radiomics nomogram for preoperatively differentiating combined hepatocellular-cholangiocarcinoma from mass-forming intrahepatic cholangiocarcinoma. Eur Radiol, 2022, 32(7): 5004-5015.
16. 周子东, 查悦明, 黄文山, 等. ¹⁸F-FDGPET-CT影像组学鉴别中低分化肝细胞癌和肝内胆管细胞癌. 中华肝脏外科手术学电子杂志, 2019, 8(2): 154-158.
17. Peng JB, Peng YT, Lin P, et al. Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics. Clin Radiol, 2022, 77(2): 104-113.
18. Xue B, Wu S, Zhang M, et al. A radiomic-based model of different contrast-enhanced CT phase for differentiate intrahepatic cholangiocarcinoma from inflammatory mass with hepatolithiasis. Abdom Radiol (NY), 2021, 46(8): 3835-3844.
19. Xue B, Wu S, Zheng M, et al. Development and validation of a radiomic-based model for prediction of intrahepatic cholangiocarcinoma in patients with intrahepatic lithiasis complicated by imagologically diagnosed mass. Front Oncol, 2021, 10: 598253. doi:10.3389/fonc.2020.598253.
20. Foley KG, Lahaye MJ, Thoeni RF, et al. Management and follow-up of gallbladder polyps: updated joint guidelines between the ESGAR, EAES, EFISDS and ESGE. Eur Radiol, 2022, 32(5): 3358-3368.
21. Yuan HX, Yu QH, Zhang YQ, et al. Ultrasound radiomics effective for preoperative identification of true and pseudo gallbladder polyps based on spatial and morphological features. Front Oncol, 2020, 10: 1719. doi:10.3389/fonc.2020.01719.
22. Zhang X, Wang J, Wu B, et al. A nomogram-based model and ultrasonic radiomic features for gallbladder polyp classification. J Gastroenterol Hepatol, 2022, 37(7): 1380-1388.
23. Han S, Liu Y, Li X, et al. Development and validation of a preoperative nomogram for predicting benign and malignant gallbladder polypoid lesions. Front Oncol, 2022, 12: 800449. doi:10.3389/fonc.2022.800449.
24. 杨晓东, 刘屹, 郭妍, 等. CT影像组学鉴别最大径≥1cm良恶性胆囊息肉. 中国医学影像技术, 2019, 35(12): 1842-1846.
25. Zhang X, Wang J, Wu B, et al. A nomogram-based model to predict neoplastic risk for patients with gallbladder polyps. J Clin Transl Hepatol, 2022, 10(2): 263-272.
26. Rizvi S, Khan SA, Hallemeier CL, et al. Cholangiocarcinoma-evolving concepts and therapeutic strategies. Nat Rev Clin Oncol, 2018, 15(2): 95-111.
27. Razumilava N, Gores GJ. Cholangiocarcinoma. Lancet, 2014, 383(9935): 2168-2179.
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