Banales JM, Marin JJG, Lamarca A, et al. Cholangiocarcinoma: the next horizon in mechanisms and management. Nat Rev Gastroenterol Hepatol. 2020;17:557–88. https://doi.org/10.1038/s41575-020-0310-z.
Article PubMed PubMed Central Google Scholar
Vogel A, Bridgewater J, Edeline J, et al. Biliary tract cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up. Ann Oncol. 2023;34:127–40. https://doi.org/10.1016/j.annonc.2022.10.506.
Article CAS PubMed Google Scholar
Hennedige TP, Neo WT, Venkatesh SK. Imaging of malignancies of the biliary tract- an update. Cancer Imaging. 2014;14:14. https://doi.org/10.1186/1470-7330-14-14.
Article PubMed PubMed Central Google Scholar
Kalage D, Gupta P, Gulati A, et al. Multiparametric MR imaging with diffusion-weighted, intravoxel incoherent motion, diffusion tensor, and dynamic contrast-enhanced perfusion sequences to assess gallbladder wall thickening: a prospective study based on surgical histopathology. Eur Radiol. 2023;33:4981–93. https://doi.org/10.1007/s00330-023-09455-w.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. https://doi.org/10.1016/j.media.2017.07.005.
Park CM, Lee JH. Deep learning for lung cancer nodal staging and real-world clinical practice. Radiology. 2022;302:212–3. https://doi.org/10.1148/radiol.2021211981.
Armato SG 3rd. Deep learning demonstrates potential for lung cancer detection in chest radiography. Radiology. 2020;297:697–8. https://doi.org/10.1148/radiol.2020203538.
Berre C, Sandborn WJ, Aridhi S, et al. Application of artificial intelligence to Gastroenterology and Hepatology. Gastroenterology. 2020;158:76-94.e2. https://doi.org/10.1053/j.gastro.2019.08.058.
Liu KL, Wu T, Chen PT, et al. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020;2:e303–13. https://doi.org/10.1016/S2589-7500(20)30078-9.
Van Calster B, Timmerman S, Geysels A, Verbakel JY, Froyman W. A deep-learning enabled diagnosis of ovarian cancer. Lancet Digit Health. 2022;4:e630. https://doi.org/10.1016/S2589-7500(22)00130-3.
Haghbin H, Aziz M. Artificial intelligence and cholangiocarcinoma: updates and prospects. World J Clin Oncol. 2022;13:125–34. https://doi.org/10.5306/wjco.v13.i2.125.
Article PubMed PubMed Central Google Scholar
Wang S, Liu X, Zhao J, et al. Computer auxiliary diagnosis technique of detecting cholangiocarcinoma based on medical imaging: A review. Comput Methods Programs Biomed. 2021;208:106265. https://doi.org/10.1016/j.cmpb.2021.106265.
Njei B, Kanmounye US, Seto N, et al. Artificial intelligence in medical imaging for cholangiocarcinoma diagnosis: a systematic review with scientometric analysis. J Gastroenterol Hepatol. 2023;38:874–82. https://doi.org/10.1111/jgh.16180.
Njei B, McCarty TR, Mohan BP, Fozo L, Navaneethan U. Artificial intelligence in endoscopic imaging for detection of malignant biliary strictures and cholangiocarcinoma: a systematic review. Ann Gastroenterol. 2023;36:223–30. https://doi.org/10.20524/aog.2023.0779.
Article PubMed PubMed Central Google Scholar
Chen B, Mao Y, Li J, et al. Predicting very early recurrence in intrahepatic cholangiocarcinoma after curative hepatectomy using machine learning radiomics based on CECT: A multi-institutional study. Comput Biol Med. 2023;167:107612. https://doi.org/10.1016/j.compbiomed.2023.107612.
Article CAS PubMed Google Scholar
Bo Z, Chen B, Yang Y, et al. Machine learning radiomics to predict the early recurrence of intrahepatic cholangiocarcinoma after curative resection: a multicentre cohort study. Eur J Nucl Med Mol Imaging. 2023;50:2501–13. https://doi.org/10.1007/s00259-023-06184-6.
Song Y, Zhou G, Zhou Y, et al. Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study, Hepatol Int. 2023;17:1016-27. https://doi.org/10.1007/s12072-023-10487-z
Jolissaint JS, Wang T, Soares KC, et al. Machine learning radiomics can predict early liver recurrence after resection of intrahepatic cholangiocarcinoma. HPB (Oxford). 2022;24:1341–50. https://doi.org/10.1016/j.hpb.2022.02.004.
Qin H, Hu X, Zhang J, et al. Machine-learning radiomics to predict early recurrence in perihilar cholangiocarcinoma after curative resection. Liver Int. 2021;41:837–50. https://doi.org/10.1111/liv.14763.
Mahmoudi S, Bernatz S, Ackermann J, et al. Computed tomography radiomics to differentiate intrahepatic cholangiocarcinoma and hepatocellular carcinoma. Clin Oncol (R Coll Radiol). 2023;35:e312–8. https://doi.org/10.1016/j.clon.2023.01.018.
Article CAS PubMed Google Scholar
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. https://doi.org/10.1155/2022/5334095.
Article PubMed PubMed Central Google Scholar
Liu X, Khalvati F, Namdar K, et al. Can machine learning radiomics provide preoperative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning? Eur Radiol. 2021;31:244–55. https://doi.org/10.1007/s00330-020-07119-7.
Article CAS PubMed Google Scholar
Hu R, Li H, Horng H, et al. Automated machine learning for differentiation of hepatocellular carcinoma from intrahepatic cholangiocarcinoma on multiphasic MRI. Sci Rep. 2022;12:7924. https://doi.org/10.1038/s41598-022-11997-w.
Article ADS CAS PubMed PubMed Central Google Scholar
Xu H, Zou X, Zhao Y, et al. Differentiation of intrahepatic cholangiocarcinoma and hepatic lymphoma based on radiomics and machine learning in contrast-enhanced computer tomography. Technol Cancer Res Treat. 2021;20:15330338211039124. https://doi.org/10.1177/15330338211039125.
Article CAS PubMed PubMed Central Google Scholar
Tang Y, Yang CM, Su S, Wang WJ, Fan LP, Shu J. Machine learning-based radiomics analysis for differentiation degree and lymphatic node metastasis of extrahepatic cholangiocarcinoma. BMC Cancer. 2021;21:1268. https://doi.org/10.1186/s12885-021-08947-6.
Article CAS PubMed PubMed Central Google Scholar
Wang Y, Shao J, Wang P, et al. Deep learning radiomics to predict regional lymph node staging for hilar cholangiocarcinoma. Front Oncol. 2021;11:721460. https://doi.org/10.3389/fonc.2021.721460.
Article PubMed PubMed Central Google Scholar
Ji GW, Zhang YD, Zhang H, et al. Biliary tract cancer at CT: a radiomics-based model to predict lymph node metastasis and survival outcomes. Radiology. 2019;290:90–8. https://doi.org/10.1148/radiol.2018181408.
Xu L, Yang P, Liang W, et al. A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics. 2019;9:5374–85. https://doi.org/10.7150/thno.34149.
Article PubMed PubMed Central Google Scholar
Liu Z, Zhu G, Jiang X, et al. Survival prediction in gallbladder cancer using ct based machine learning. Front Oncol. 2020;10:604288. https://doi.org/10.3389/fonc.2020.604288.
Article PubMed PubMed Central Google Scholar
Tang Y, Zhang T, Zhou X, et al. The preoperative prognostic value of the radiomics nomogram based on CT combined with machine learning in patients with intrahepatic cholangiocarcinoma. World J Surg Oncol. 2021;19:45. https://doi.org/10.1186/s12957-021-02162-0.
Article PubMed PubMed Central Google Scholar
Li MD, Lu XZ, Liu JF, et al. Preoperative survival prediction in intrahepatic cholangiocarcinoma using an ultrasound-based radiographic-radiomics signature. J Ultrasound Med. 2022;41:1483–95. https://doi.org/10.1002/jum.15833.
Liu X, Liang X, Ruan L, Yan S. A clinical-radiomics nomogram for preoperative prediction of lymph node metastasis in gallbladder cancer. Front Oncol. 2021;11:633852. https://doi.org/10.3389/fonc.2021.633852.
Article CAS PubMed PubMed Central Google Scholar
Meng FX, Zhang JX, Guo YR, et al. Contrast-enhanced CT-based deep learning radiomics nomogram for the survival prediction in gallbladder cancer postoperative. Acad Radiol. 2023:S1076-6332(23)00663-3; https://doi.org/10.1016/j.acra.2023.11.027
Zhu Y, Mao Y, Chen J, et al. Value of contrast-enhanced CT texture analysis in predicting IDH mutation status of intrahepatic cholangiocarcinoma. Sci Rep. 2021;11:6933. https://doi.org/10.1038/s41598-021-86497-4.
Article ADS CAS PubMed PubMed Central Google Scholar
Zhang J, Wu Z, Zhang X, et al. Machine learning: an approach to preoperatively predict PD-1/PD-L1 expression and outcome in intrahepatic cholangiocarcinoma using MRI biomarkers. ESMO Open. 2020;5:e000910. https://doi.org/10.1136/esmoopen-2020-000910.
Comments (0)