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For: Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol. 2018;24:121-127. [PMID: 29770763 DOI: 10.5152/dir.2018.17467] [Cited by in Crossref: 64] [Cited by in F6Publishing: 73] [Article Influence: 16.0] [Reference Citation Analysis]
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2 Peng J, Huang J, Huang G, Zhang J. Predicting the Initial Treatment Response to Transarterial Chemoembolization in Intermediate-Stage Hepatocellular Carcinoma by the Integration of Radiomics and Deep Learning. Front Oncol 2021;11:730282. [PMID: 34745952 DOI: 10.3389/fonc.2021.730282] [Reference Citation Analysis]
3 Chong HH, Yang L, Sheng RF, Yu YL, Wu DJ, Rao SX, Yang C, Zeng MS. Multi-scale and multi-parametric radiomics of gadoxetate disodium-enhanced MRI predicts microvascular invasion and outcome in patients with solitary hepatocellular carcinoma ≤ 5 cm. Eur Radiol 2021;31:4824-38. [PMID: 33447861 DOI: 10.1007/s00330-020-07601-2] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
4 Liao H, Zhang Z, Chen J, Liao M, Xu L, Wu Z, Yuan K, Song B, Zeng Y. Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8+ T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography. Ann Surg Oncol. 2019;26:4537-4547. [PMID: 31520208 DOI: 10.1245/s10434-019-07815-9] [Cited by in Crossref: 22] [Cited by in F6Publishing: 20] [Article Influence: 7.3] [Reference Citation Analysis]
5 Wang Q, Li C, Zhang J, Hu X, Fan Y, Ma K, Sparrelid E, Brismar TB. Radiomics Models for Predicting Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Radiomics Quality Score Assessment. Cancers (Basel) 2021;13:5864. [PMID: 34831018 DOI: 10.3390/cancers13225864] [Reference Citation Analysis]
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7 Xu X, Zhang HL, Liu QP, Sun SW, Zhang J, Zhu FP, Yang G, Yan X, Zhang YD, Liu XS. Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma. J Hepatol. 2019;70:1133-1144. [PMID: 30876945 DOI: 10.1016/j.jhep.2019.02.023] [Cited by in Crossref: 121] [Cited by in F6Publishing: 124] [Article Influence: 40.3] [Reference Citation Analysis]
8 Chen Y, Xia Y, Tolat PP, Long L, Jiang Z, Huang Z, Tang Q. Comparison of Conventional Gadoxetate Disodium-Enhanced MRI Features and Radiomics Signatures With Machine Learning for Diagnosing Microvascular Invasion. AJR Am J Roentgenol 2021;216:1510-20. [PMID: 33826360 DOI: 10.2214/AJR.20.23255] [Reference Citation Analysis]
9 Zhang XP, Chai ZT, Feng JK, Zhu HM, Zhang F, Hu YR, Zhong CQ, Chen ZH, Wang K, Shi J, Guo WX, Chen CS, Wu MC, Lau WY, Cheng SQ. Association of type 2 diabetes mellitus with incidences of microvascular invasion and survival outcomes in hepatitis B virus-related hepatocellular carcinoma after liver resection: A multicenter study. Eur J Surg Oncol 2021:S0748-7983(21)00680-6. [PMID: 34452770 DOI: 10.1016/j.ejso.2021.08.010] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
10 Liu Q, Li J, Liu F, Yang W, Ding J, Chen W, Wei Y, Li B, Zheng L. A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy. Cancer Imaging 2020;20:82. [PMID: 33198809 DOI: 10.1186/s40644-020-00360-9] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
11 Ke R, Cai Q, Chen Y, Lv L, Jiang Y. Diagnosis and treatment of microvascular invasion in hepatocellular carcinoma. Eur Surg 2020;52:55-68. [DOI: 10.1007/s10353-019-0573-1] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
12 Fang S, Lai L, Zhu J, Zheng L, Xu Y, Chen W, Wu F, Wu X, Chen M, Weng Q, Ji J, Zhao Z, Tu J. A Radiomics Signature-Based Nomogram to Predict the Progression-Free Survival of Patients With Hepatocellular Carcinoma After Transcatheter Arterial Chemoembolization Plus Radiofrequency Ablation. Front Mol Biosci 2021;8:662366. [PMID: 34532340 DOI: 10.3389/fmolb.2021.662366] [Reference Citation Analysis]
13 Jiang H, Liu X, Chen J, Wei Y, Lee JM, Cao L, Wu Y, Duan T, Li X, Ma L, Song B. Man or machine?Cancer Imaging. 2019;19:84. [PMID: 31806050 DOI: 10.1186/s40644-019-0266-9] [Cited by in Crossref: 10] [Cited by in F6Publishing: 10] [Article Influence: 3.3] [Reference Citation Analysis]
14 Yardımcı AH, Koçak B, Turan Bektaş C, Sel İ, Yarıkkaya E, Dursun N, Bektaş H, Usul Afşar Ç, Gürsu RU, Kılıçkesmez Ö. Tubular gastric adenocarcinoma: machine learning-based CT texture analysis for predicting lymphovascular and perineural invasion. Diagn Interv Radiol 2020;26:515-22. [PMID: 32990246 DOI: 10.5152/dir.2020.19507] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 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]
16 Li Q, Qi L, Feng QX, Liu C, Sun SW, Zhang J, Yang G, Ge YQ, Zhang YD, Liu XS. Machine Learning-Based Computational Models Derived From Large-Scale Radiographic-Radiomic Images Can Help Predict Adverse Histopathological Status of Gastric Cancer. Clin Transl Gastroenterol. 2019;10:e00079. [PMID: 31577560 DOI: 10.14309/ctg.0000000000000079] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 3.0] [Reference Citation Analysis]
17 Yang JQ, Zeng R, Cao JM, Wu CQ, Chen TW, Li R, Zhang XM, Ou J, Li HJ, Mu QW. Predicting gastro-oesophageal variceal bleeding in hepatitis B-related cirrhosis by CT radiomics signature. Clin Radiol 2019;74:976.e1-9. [PMID: 31604574 DOI: 10.1016/j.crad.2019.08.028] [Cited by in Crossref: 2] [Article Influence: 0.7] [Reference Citation Analysis]
18 Malloy PC. Combination Therapy in Intermediate-Stage Hepatocellular Carcinoma: Do We Need to Know about Microvascular Invasion? Radiology 2019;292:248-9. [PMID: 31136261 DOI: 10.1148/radiol.2019190921] [Cited by in Crossref: 1] [Article Influence: 0.3] [Reference Citation Analysis]
19 Chong H, Zhou P, Yang C, Zeng M. An excellent nomogram predicts microvascular invasion that cannot independently stratify outcomes of small hepatocellular carcinoma. Ann Transl Med 2021;9:757. [PMID: 34268370 DOI: 10.21037/atm-20-7952] [Reference Citation Analysis]
20 Miranda Magalhaes Santos JM, Clemente Oliveira B, Araujo-Filho JAB, Assuncao-Jr AN, de M Machado FA, Carlos Tavares Rocha C, Horvat JV, Menezes MR, Horvat N. State-of-the-art in radiomics of hepatocellular carcinoma: a review of basic principles, applications, and limitations. Abdom Radiol (NY). 2020;45:342-353. [PMID: 31707435 DOI: 10.1007/s00261-019-02299-3] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 15.0] [Reference Citation Analysis]
21 Hu MJ, Yu YX, Fan YF, Hu CH. CT-based radiomics model to distinguish necrotic hepatocellular carcinoma from pyogenic liver abscess. Clin Radiol 2021;76:161.e11-7. [PMID: 33267948 DOI: 10.1016/j.crad.2020.11.002] [Reference Citation Analysis]
22 Sarioglu FC, Sarioglu O, Guleryuz H, Ozer E, Ince D, Olgun HN. MRI-based texture analysis for differentiating pediatric craniofacial rhabdomyosarcoma from infantile hemangioma. Eur Radiol 2020;30:5227-36. [PMID: 32382846 DOI: 10.1007/s00330-020-06908-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
23 Sun Y, Bai H, Xia W, Wang D, Zhou B, Zhao X, Yang G, Xu L, Zhang W, Liu P, Xu J, Meng S, Liu R, Gao X. Predicting the Outcome of Transcatheter Arterial Embolization Therapy for Unresectable Hepatocellular Carcinoma Based on Radiomics of Preoperative Multiparameter MRI. J Magn Reson Imaging 2020;52:1083-90. [PMID: 32233054 DOI: 10.1002/jmri.27143] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
24 Huang Y, Zeng H, Chen L, Luo Y, Ma X, Zhao Y. Exploration of an Integrative Prognostic Model of Radiogenomics Features With Underlying Gene Expression Patterns in Clear Cell Renal Cell Carcinoma. Front Oncol 2021;11:640881. [PMID: 33763374 DOI: 10.3389/fonc.2021.640881] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Zhang C, Zhao R, Chen F, Zhu Y, Chen L. Preoperative prediction of microvascular invasion in non-metastatic hepatocellular carcinoma based on nomogram analysis. Transl Oncol 2021;14:100875. [PMID: 32979686 DOI: 10.1016/j.tranon.2020.100875] [Reference Citation Analysis]
26 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]
27 Shi F, Zhou Z, Huang X, Liu Q, Lin A. Is anatomical resection necessary for early hepatocellular carcinoma? A single institution retrospective experience. Future Oncology 2019;15:2041-51. [DOI: 10.2217/fon-2019-0117] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 1.3] [Reference Citation Analysis]
28 Zhang T, Pandey G, Xu L, Chen W, Gu L, Wu Y, Chen X. The Value of TTPVI in Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Cancer Manag Res 2020;12:4097-105. [PMID: 32581583 DOI: 10.2147/CMAR.S245475] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
29 Peng J, Kang S, Ning Z, Deng H, Shen J, Xu Y, Zhang J, Zhao W, Li X, Gong W, Huang J, Liu L. Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging. Eur Radiol. 2020;30:413-424. [PMID: 31332558 DOI: 10.1007/s00330-019-06318-1] [Cited by in Crossref: 29] [Cited by in F6Publishing: 22] [Article Influence: 9.7] [Reference Citation Analysis]
30 Zhang L, Zhou H, Gu D, Tian J, Zhang B, Dong D, Mo X, Liu J, Luo X, Pei S, Dong Y, Huang W, Chen Q, Liang C, Lian Z, Zhang S. Radiomic Nomogram: Pretreatment Evaluation of Local Recurrence in Nasopharyngeal Carcinoma based on MR Imaging. J Cancer 2019;10:4217-25. [PMID: 31413740 DOI: 10.7150/jca.33345] [Cited by in Crossref: 15] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
31 Liu W, Liu X, Peng M, Chen GQ, Liu PH, Cui XW, Jiang F, Dietrich CF. Artificial intelligence for hepatitis evaluation. World J Gastroenterol 2021; 27(34): 5715-5726 [PMID: 34629796 DOI: 10.3748/wjg.v27.i34.5715] [Reference Citation Analysis]
32 Cao JM, Yang JQ, Ming ZQ, Wu JL, Yang LQ, Chen TW, Li R, Ou J, Zhang XM, Mu QW, Li HJ, Hu J. A radiomics model of liver CT to predict risk of hepatic encephalopathy secondary to hepatitis B related cirrhosis.Eur J Radiol. 2020;130:109201. [PMID: 32738462 DOI: 10.1016/j.ejrad.2020.109201] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
33 Huang J, Tian W, Zhang L, Huang Q, Lin S, Ding Y, Liang W, Zheng S. Preoperative Prediction Power of Imaging Methods for Microvascular Invasion in Hepatocellular Carcinoma: A Systemic Review and Meta-Analysis. Front Oncol 2020;10:887. [PMID: 32676450 DOI: 10.3389/fonc.2020.00887] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 3.5] [Reference Citation Analysis]
34 He M, Zhang P, Ma X, He B, Fang C, Jia F. Radiomic Feature-Based Predictive Model for Microvascular Invasion in Patients With Hepatocellular Carcinoma. Front Oncol. 2020;10:574228. [PMID: 33251138 DOI: 10.3389/fonc.2020.574228] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
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44 Yu XP, Wang L, Yu HY, Zou YW, Wang C, Jiao JW, Hong H, Zhang S. MDCT-Based Radiomics Features for the Differentiation of Serous Borderline Ovarian Tumors and Serous Malignant Ovarian Tumors. Cancer Manag Res 2021;13:329-36. [PMID: 33488120 DOI: 10.2147/CMAR.S284220] [Reference Citation Analysis]
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54 Peng JB, Peng YT, Lin P, Wan D, Qin H, Li X, Wang XR, He Y, Yang H. Differentiating infected focal liver lesions from malignant mimickers: value of ultrasound-based radiomics. Clin Radiol 2021:S0009-9260(21)00486-4. [PMID: 34753587 DOI: 10.1016/j.crad.2021.10.009] [Reference Citation Analysis]
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56 Hu J, Zhao Y, Li M, Liu J, Wang F, Weng Q, Wang X, Cao D. Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI. Eur J Radiol 2020;131:109251. [PMID: 32916409 DOI: 10.1016/j.ejrad.2020.109251] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
57 Min JH, Lee MW, Park HS, Lee DH, Park HJ, Lim S, Choi SY, Lee J, Lee JE, Ha SY, Cha DI, Carriere KC, Ahn JH. Interobserver Variability and Diagnostic Performance of Gadoxetic Acid-enhanced MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma. Radiology 2020;297:573-81. [PMID: 32990512 DOI: 10.1148/radiol.2020201940] [Cited by in Crossref: 4] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
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