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For: Wang XH, Long LH, Cui Y, Jia AY, Zhu XG, Wang HZ, Wang Z, Zhan CM, Wang ZH, Wang WH. MRI-based radiomics model for preoperative prediction of 5-year survival in patients with hepatocellular carcinoma. Br J Cancer 2020;122:978-85. [PMID: 31937925 DOI: 10.1038/s41416-019-0706-0] [Cited by in Crossref: 14] [Cited by in F6Publishing: 18] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Li L, Xu L, Zhou S, Wang P, Zhang M, Li B. Tumour site is a risk factor for hepatocellular carcinoma after hepatectomy: a 1:2 propensity score matching analysis. BMC Surg 2022;22:104. [PMID: 35313888 DOI: 10.1186/s12893-022-01564-5] [Reference Citation Analysis]
2 Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2021. [PMID: 34455593 DOI: 10.1002/mp.15195] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Tang YY, Zhao YN, Zhang T, Chen ZY, Ma XL. Comprehensive radiomics nomogram for predicting survival of patients with combined hepatocellular carcinoma and cholangiocarcinoma. World J Gastroenterol 2021; 27(41): 7173-7189 [PMID: 34887636 DOI: 10.3748/wjg.v27.i41.7173] [Reference Citation Analysis]
4 Tian Y, Komolafe TE, Chen T, Zhou B, Yang X. Prediction of TACE Treatment Response in a Preoperative MRI via Analysis of Integrating Deep Learning and Radiomics Features. J Med Biol Eng . [DOI: 10.1007/s40846-022-00692-w] [Reference Citation Analysis]
5 Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S. Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol (NY) 2021;46:3674-85. [PMID: 33891149 DOI: 10.1007/s00261-021-03085-w] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 8.0] [Reference Citation Analysis]
6 Chi X, Jiang L, Yuan Y, Huang X, Yang X, Hochwald S, Liu J, Huang H. A comparison of clinical pathologic characteristics between alpha-fetoprotein negative and positive hepatocellular carcinoma patients from Eastern and Southern China. BMC Gastroenterol 2022;22. [DOI: 10.1186/s12876-022-02279-w] [Reference Citation Analysis]
7 Tian Y, Komolafe TE, Zheng J, Zhou G, Chen T, Zhou B, Yang X. Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features. Diagnostics (Basel) 2021;11:1875. [PMID: 34679573 DOI: 10.3390/diagnostics11101875] [Reference Citation Analysis]
8 Fountzilas C, Evans R, Alaklabi S, Iyer R. Immunotherapy in hepatocellular cancer. Adv Cancer Res 2021;149:295-320. [PMID: 33579426 DOI: 10.1016/bs.acr.2020.12.002] [Reference Citation Analysis]
9 Nie Z, Zhao P, Shang Y, Sun B. Nomograms to predict the prognosis in locally advanced oral squamous cell carcinoma after curative resection. BMC Cancer 2021;21:372. [PMID: 33827452 DOI: 10.1186/s12885-021-08106-x] [Reference Citation Analysis]
10 Zou ZM, Chang DH, Liu H, Xiao YD. Current updates in machine learning in the prediction of therapeutic outcome of hepatocellular carcinoma: what should we know? Insights Imaging 2021;12:31. [PMID: 33675433 DOI: 10.1186/s13244-021-00977-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
11 Tang X, Liang J, Xiang B, Yuan C, Wang L, Zhu B, Ge X, Fang M, Ding Z. Positron Emission Tomography/Magnetic Resonance Imaging Radiomics in Predicting Lung Adenocarcinoma and Squamous Cell Carcinoma. Front Oncol 2022;12:803824. [PMID: 35186742 DOI: 10.3389/fonc.2022.803824] [Reference Citation Analysis]
12 Dong Y, Zuo D, Qiu YJ, Cao JY, Wang HZ, Yu LY, Wang WP. Preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma based on kupffer phase radiomic features of sonazoid contrast-enhanced ultrasound (SCEUS): A prospective study. Clin Hemorheol Microcirc 2022. [PMID: 35001883 DOI: 10.3233/CH-211363] [Reference Citation Analysis]
13 Nazari M, Shiri I, Zaidi H. Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput Biol Med 2021;129:104135. [PMID: 33254045 DOI: 10.1016/j.compbiomed.2020.104135] [Cited by in Crossref: 11] [Cited by in F6Publishing: 6] [Article Influence: 5.5] [Reference Citation Analysis]
14 Gong XQ, Tao YY, Wu YK, Liu N, Yu X, Wang R, Zheng J, Liu N, Huang XH, Li JD, Yang G, Wei XQ, Yang L, Zhang XM. Progress of MRI Radiomics in Hepatocellular Carcinoma. Front Oncol 2021;11:698373. [PMID: 34616673 DOI: 10.3389/fonc.2021.698373] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
15 Zhang L, Hu J, Hou J, Jiang X, Guo L, Tian L. Radiomics-based model using gadoxetic acid disodium-enhanced MR images: associations with recurrence-free survival of patients with hepatocellular carcinoma treated by surgical resection. Abdom Radiol (NY) 2021;46:3845-54. [PMID: 33733337 DOI: 10.1007/s00261-021-03034-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
16 Maruyama H, Yamaguchi T, Nagamatsu H, Shiina S. AI-Based Radiological Imaging for HCC: Current Status and Future of Ultrasound. Diagnostics (Basel) 2021;11:292. [PMID: 33673229 DOI: 10.3390/diagnostics11020292] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
17 Brancato V, Garbino N, Salvatore M, Cavaliere C. MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma. Diagnostics 2022;12:1085. [DOI: 10.3390/diagnostics12051085] [Reference Citation Analysis]
18 Zhao Y, Wang N, Wu J, Zhang Q, Lin T, Yao Y, Chen Z, Wang M, Sheng L, Liu J, Song Q, Wang F, An X, Guo Y, Li X, Wu T, Liu AL. Radiomics Analysis Based on Contrast-Enhanced MRI for Prediction of Therapeutic Response to Transarterial Chemoembolization in Hepatocellular Carcinoma. Front Oncol. 2021;11:582788. [PMID: 33868988 DOI: 10.3389/fonc.2021.582788] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
19 Miles K. Radiomics for personalised medicine: the long road ahead. Br J Cancer 2020;122:929-30. [PMID: 31937924 DOI: 10.1038/s41416-019-0699-8] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
20 Li G, An C, Yu J, Huang Q. Radiomics analysis of ultrasonic image predicts sensitive effects of microwave ablation in treatment of patient with benign breast tumors. Biomedical Signal Processing and Control 2022;76:103722. [DOI: 10.1016/j.bspc.2022.103722] [Reference Citation Analysis]
21 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. [PMID: 34819043 DOI: 10.1186/s12885-021-08947-6] [Reference Citation Analysis]
22 Zhao Y, Wu J, Zhang Q, Hua Z, Qi W, Wang N, Lin T, Sheng L, Cui D, Liu J, Song Q, Li X, Wu T, Guo Y, Cui J, Liu A. Radiomics Analysis Based on Multiparametric MRI for Predicting Early Recurrence in Hepatocellular Carcinoma After Partial Hepatectomy. J Magn Reson Imaging. 2020;. [PMID: 33217114 DOI: 10.1002/jmri.27424] [Cited by in Crossref: 3] [Cited by in F6Publishing: 7] [Article Influence: 1.5] [Reference Citation Analysis]
23 Sim KC, Kim MJ, Cho Y, Kim HJ, Park BJ, Sung DJ, Han YE, Han NY, Kim TH, Lee YJ. Diagnostic Feasibility of Magnetic Resonance Elastography Radiomics Analysis for the Assessment of Hepatic Fibrosis in Patients With Nonalcoholic Fatty Liver Disease. J Comput Assist Tomogr 2022. [PMID: 35483092 DOI: 10.1097/RCT.0000000000001308] [Reference Citation Analysis]
24 Zheng RR, Cai MT, Lan L, Huang XW, Yang YJ, Powell M, Lin F. An MRI-based radiomics signature and clinical characteristics for survival prediction in early-stage cervical cancer. Br J Radiol 2022;95:20210838. [PMID: 34797703 DOI: 10.1259/bjr.20210838] [Reference Citation Analysis]
25 Cui L, Han S, Qi S, Duan Y, Kang Y, Luo Y. Deep symmetric three-dimensional convolutional neural networks for identifying acute ischemic stroke via diffusion-weighted images. J Xray Sci Technol 2021;29:551-66. [PMID: 33967077 DOI: 10.3233/XST-210861] [Reference Citation Analysis]
26 Fan L, Cao Q, Ding X, Gao D, Yang Q, Li B. Radiotranscriptomics signature-based predictive nomograms for radiotherapy response in patients with nonsmall cell lung cancer: Combination and association of CT features and serum miRNAs levels. Cancer Med 2020;9:5065-74. [PMID: 32458566 DOI: 10.1002/cam4.3115] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
27 Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13(11): 1599-1615 [PMID: 34853638 DOI: 10.4251/wjgo.v13.i11.1599] [Reference Citation Analysis]
28 Chong H, Gong Y, Zhang Y, Dai Y, Sheng R, Zeng M. Radiomics on Gadoxetate Disodium-enhanced MRI: Non-invasively Identifying Glypican 3-Positive Hepatocellular Carcinoma and Postoperative Recurrence. Acad Radiol 2022:S1076-6332(22)00254-9. [PMID: 35562264 DOI: 10.1016/j.acra.2022.04.006] [Reference Citation Analysis]
29 Chong H, Gong Y, Pan X, Liu A, Chen L, Yang C, Zeng M. Peritumoral Dilation Radiomics of Gadoxetate Disodium-Enhanced MRI Excellently Predicts Early Recurrence of Hepatocellular Carcinoma without Macrovascular Invasion After Hepatectomy. J Hepatocell Carcinoma 2021;8:545-63. [PMID: 34136422 DOI: 10.2147/JHC.S309570] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
30 Reginelli A, Nardone V, Giacobbe G, Belfiore MP, Grassi R, Schettino F, Del Canto M, Grassi R, Cappabianca S. Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021;11:1796. [PMID: 34679494 DOI: 10.3390/diagnostics11101796] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]