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For: 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]
Number Citing Articles
1 Homayounieh F, Saini S, Mostafavi L, Doda Khera R, Sühling M, Schmidt B, Singh R, Flohr T, Kalra MK. Accuracy of radiomics for differentiating diffuse liver diseases on non-contrast CT. Int J CARS 2020;15:1727-36. [DOI: 10.1007/s11548-020-02212-0] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 Fiz F, Costa G, Gennaro N, la Bella L, Boichuk A, Sollini M, Politi LS, Balzarini L, Torzilli G, Chiti A, Viganò L. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment. Diagnostics (Basel) 2021;11:1162. [PMID: 34202253 DOI: 10.3390/diagnostics11071162] [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 Zhou JM, Zhou CY, Chen XP, Zhang ZW. Anatomic resection improved the long-term outcome of hepatocellular carcinoma patients with microvascular invasion: A prospective cohort study. World J Gastrointest Oncol 2021; 13(12): 2190-2202 [DOI: 10.4251/wjgo.v13.i12.2190] [Reference Citation Analysis]
5 Zhang D, Wei Q, Wu GG, Zhang XY, Lu WW, Lv WZ, Liao JT, Cui XW, Ni XJ, Dietrich CF. Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound. Front Oncol 2021;11:709339. [PMID: 34557410 DOI: 10.3389/fonc.2021.709339] [Reference Citation Analysis]
6 Caruso D, Polici M, Zerunian M, Pucciarelli F, Guido G, Polidori T, Landolfi F, Nicolai M, Lucertini E, Tarallo M, Bracci B, Nacci I, Rucci C, Iannicelli E, Laghi A. Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI. Cancers (Basel) 2021;13:2522. [PMID: 34063937 DOI: 10.3390/cancers13112522] [Reference Citation Analysis]
7 Zhang Y, Shu Z, Ye Q, Chen J, Zhong J, Jiang H, Wu C, Yu T, Pang P, Ma T, Lin C. Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma via Multi-Parametric MRI Radiomics. Front Oncol 2021;11:633596. [PMID: 33747956 DOI: 10.3389/fonc.2021.633596] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Zhang J, Wang G, Ren J, Yang Z, Li D, Cui Y, Yang X. Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma. Eur Radiol. [DOI: 10.1007/s00330-021-08504-6] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
9 Qian X, Lu X, Ma X, Zhang Y, Zhou C, Wang F, Shi Y, Zeng M. A Multi-Parametric Radiomics Nomogram for Preoperative Prediction of Microvascular Invasion Status in Intrahepatic Cholangiocarcinoma. Front Oncol 2022;12:838701. [PMID: 35280821 DOI: 10.3389/fonc.2022.838701] [Reference Citation Analysis]
10 Ye S, Chen C, Bai Z, Wang J, Yao X, Nedzvedz O. Intelligent Labeling of Tumor Lesions Based on Positron Emission Tomography/Computed Tomography. Sensors (Basel) 2022;22:5171. [PMID: 35890851 DOI: 10.3390/s22145171] [Reference Citation Analysis]
11 Bai H, Meng S, Xiong C, Liu Z, Shi W, Ren Q, Xia W, Zhao X, Jian J, Song Y, Ni C, Gao X, Li Z. Preoperative CECT-based Radiomic Signature for Predicting the Response of Transarterial Chemoembolization (TACE) Therapy in Hepatocellular Carcinoma. Cardiovasc Intervent Radiol 2022. [PMID: 35896687 DOI: 10.1007/s00270-022-03221-z] [Reference Citation Analysis]
12 Jia P, Mao Y, Liu K, Wei X, Gupta SK. The Value of Color Doppler Ultrasound and CT Combined with Serum AFP Examination in the Diagnosis of Hepatocellular Carcinoma. Journal of Healthcare Engineering 2022;2022:1-7. [DOI: 10.1155/2022/4147753] [Reference Citation Analysis]
13 Zhang J, Huang S, Xu Y, Wu J. Diagnostic Accuracy of Artificial Intelligence Based on Imaging Data for Preoperative Prediction of Microvascular Invasion in Hepatocellular Carcinoma: A Systematic Review and Meta-Analysis. Front Oncol 2022;12:763842. [PMID: 35280776 DOI: 10.3389/fonc.2022.763842] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Yao W, Yang S, Ge Y, Fan W, Xiang L, Wan Y, Gu K, Zhao Y, Zha R, Bu J. Computed Tomography Radiomics-Based Prediction of Microvascular Invasion in Hepatocellular Carcinoma. Front Med (Lausanne) 2022;9:819670. [PMID: 35402463 DOI: 10.3389/fmed.2022.819670] [Reference Citation Analysis]
15 Wang Y, Zhou CW, Zhu GQ, Li N, Qian XL, Chong HH, Yang C, Zeng MS. A multidimensional nomogram combining imaging features and clinical factors to predict the invasiveness and metastasis of combined hepatocellular cholangiocarcinoma. Ann Transl Med 2021;9:1518. [PMID: 34790724 DOI: 10.21037/atm-21-2500] [Reference Citation Analysis]
16 Li L, Lin Y, Yu D, Liu Z, Gao Y, Qiao J. A Multi-Organ Fusion and LightGBM Based Radiomics Algorithm for High-Risk Esophageal Varices Prediction in Cirrhotic Patients. IEEE Access 2021;9:15041-52. [DOI: 10.1109/access.2021.3052776] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 6.0] [Reference Citation Analysis]
17 Xu F, Zhu W, Shen Y, Wang J, Xu R, Qutesh C, Song L, Gan Y, Pu C, Hu H. Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma. Front Oncol 2020;10:872. [PMID: 32850301 DOI: 10.3389/fonc.2020.00872] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
18 Sun BY, Gu PY, Guan RY, Zhou C, Lu JW, Yang ZF, Pan C, Zhou PY, Zhu YP, Li JR, Wang ZT, Gao SS, Gan W, Yi Y, Luo Y, Qiu SJ. Deep-learning-based analysis of preoperative MRI predicts microvascular invasion and outcome in hepatocellular carcinoma. World J Surg Oncol 2022;20:189. [PMID: 35676669 DOI: 10.1186/s12957-022-02645-8] [Reference Citation Analysis]
19 Emrich T, Hahn F, Fleischmann D, Halfmann MC, Düber C, Varga-Szemes A, Escher F, Pefani E, Münzel T, Schultheiss HP, Kreitner KF, Wenzel P. T1 and T2 mapping to detect chronic inflammation in cardiac magnetic resonance imaging in heart failure with reduced ejection fraction. ESC Heart Fail 2020;7:2544-52. [PMID: 32790159 DOI: 10.1002/ehf2.12830] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
20 Che F, Xu Q, Li Q, Huang ZX, Yang CW, Wang LY, Wei Y, Shi YJ, Song B. Radiomics signature: A potential biomarker for β-arrestin1 phosphorylation prediction in hepatocellular carcinoma. World J Gastroenterol 2022; 28(14): 1479-1493 [DOI: 10.3748/wjg.v28.i14.1479] [Reference Citation Analysis]
21 Song L, Li J, Luo Y. The importance of a nonsmooth tumor margin and incomplete tumor capsule in predicting HCC microvascular invasion on preoperative imaging examination: a systematic review and meta-analysis. Clin Imaging. 2020;76:77-82. [PMID: 33578134 DOI: 10.1016/j.clinimag.2020.11.057] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
22 Shaghaghi M, AliyariG Hasabeh M, Ameli S, Ghadimi M, Hazhirkarzar B, Rezvani Habibabadi R, Tang H, Khoshpouri P, Wu Q, Pandey A, Pandey P, Baghdadi A, Kamel IR. Role of tumor margin and ADC change in defining the need for additional treatments after the first TACE in patients with unresectable HCC. Eur J Radiol 2020;133:109389. [PMID: 33166831 DOI: 10.1016/j.ejrad.2020.109389] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
23 Xie T, Wang X, Li M, Tong T, Yu X, Zhou Z. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection. Eur Radiol. 2020;30:2513-2524. [PMID: 32006171 DOI: 10.1007/s00330-019-06600-2] [Cited by in Crossref: 11] [Cited by in F6Publishing: 12] [Article Influence: 5.5] [Reference Citation Analysis]
24 Wei JW, Fu SR, Zhang J, Gu DS, Li XQ, Chen XD, Zhang ST, He XF, Yan JF, Lu LG, Tian J. CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study. Hepatobiliary Pancreat Dis Int 2021:S1499-3872(21)00198-3. [PMID: 34674948 DOI: 10.1016/j.hbpd.2021.09.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
25 Zhang Y, Yu S, Zhang L, Kang L. Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method. Front Oncol 2020;10:1121. [PMID: 32850321 DOI: 10.3389/fonc.2020.01121] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
26 Dreher C, Linde P, Boda-Heggemann J, Baessler B. Radiomics for liver tumours. Strahlenther Onkol 2020;196:888-99. [PMID: 32296901 DOI: 10.1007/s00066-020-01615-x] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
27 Mo Q, Liu Y, Zhou Z, Li R, Gong W, Xiang B, Tang W, Yu H. Prognostic Value of Aspartate Transaminase/Alanine Transaminase Ratio in Patients With Hepatitis B Virus-Related Hepatocellular Carcinoma Undergoing Hepatectomy. Front Oncol 2022;12:876900. [PMID: 35664791 DOI: 10.3389/fonc.2022.876900] [Reference Citation Analysis]
28 Hu X, Sun X, Hu F, Liu F, Ruan W, Wu T, An R, Lan X. Multivariate radiomics models based on 18F-FDG hybrid PET/MRI for distinguishing between Parkinson's disease and multiple system atrophy. Eur J Nucl Med Mol Imaging 2021;48:3469-81. [PMID: 33829415 DOI: 10.1007/s00259-021-05325-z] [Reference Citation Analysis]
29 Li L, Kan X, Zhao Y, Liang B, Ye T, Yang L, Zheng C. Radiomics Signature: A potential biomarker for the prediction of survival in Advanced Hepatocellular Carcinoma. Int J Med Sci 2021;18:2276-84. [PMID: 33967603 DOI: 10.7150/ijms.55510] [Reference Citation Analysis]
30 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]
31 Zhang W, Liu Z, Chen J, Dong S, Cen B, Zheng S, Xu X. A preoperative model for predicting microvascular invasion and assisting in prognostic stratification in liver transplantation for HCC regarding empirical criteria. Transl Oncol 2021;14:101200. [PMID: 34399173 DOI: 10.1016/j.tranon.2021.101200] [Reference Citation Analysis]
32 Liu X, Khalvati F, Namdar K, Fischer S, Lewis S, Taouli B, Haider MA, Jhaveri KS. Can machine learning radiomics provide pre-operative differentiation of combined hepatocellular cholangiocarcinoma from hepatocellular carcinoma and cholangiocarcinoma to inform optimal treatment planning?Eur Radiol. 2021;31:244-255. [PMID: 32749585 DOI: 10.1007/s00330-020-07119-7] [Cited by in Crossref: 4] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
33 Yu Y, Fan Y, Wang X, Zhu M, Hu M, Shi C, Hu C. Gd-EOB-DTPA-enhanced MRI radiomics to predict vessels encapsulating tumor clusters (VETC) and patient prognosis in hepatocellular carcinoma. Eur Radiol 2021. [PMID: 34480625 DOI: 10.1007/s00330-021-08250-9] [Reference Citation Analysis]
34 Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol 2021;36:569-80. [PMID: 33709606 DOI: 10.1111/jgh.15415] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
35 Yang Y, Lin K, Liu L, Qian Y, Yang Y, Yuan S, Zhu P, Huang J, Liu F, Gu F, Fu S, Jiang B, Liu H, Pan Z, Lau WY, Zhou W. Impact of preoperative TACE on incidences of microvascular invasion and long-term post-hepatectomy survival in hepatocellular carcinoma patients: A propensity score matching analysis. Cancer Med 2021;10:2100-11. [PMID: 33650288 DOI: 10.1002/cam4.3814] [Reference Citation Analysis]
36 Zhang J, Huang Z, Cao L, Zhang Z, Wei Y, Zhang X, Song B. Differentiation combined hepatocellular and cholangiocarcinoma from intrahepatic cholangiocarcinoma based on radiomics machine learning. Ann Transl Med 2020;8:119. [PMID: 32175412 DOI: 10.21037/atm.2020.01.126] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
37 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] [Cited by in CrossRef: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
38 Wu J, Liang F, Wei R, Lai S, Lv X, Luo S, Wu Z, Chen H, Zhang W, Zeng X, Ye X, Wu Y, Wei X, Jiang X, Zhen X, Yang R. A Multiparametric MR-Based RadioFusionOmics Model with Robust Capabilities of Differentiating Glioblastoma Multiforme from Solitary Brain Metastasis. Cancers (Basel) 2021;13:5793. [PMID: 34830943 DOI: 10.3390/cancers13225793] [Reference Citation Analysis]
39 Wang L, Zhang Y, Chen Y, Tan J, Wang L, Zhang J, Yang C, Ma Q, Ge Y, Xu Z, Pan Z, Du L, Yan F, Yao W, Zhang H. The Performance of a Dual-Energy CT Derived Radiomics Model in Differentiating Serosal Invasion for Advanced Gastric Cancer Patients After Neoadjuvant Chemotherapy: Iodine Map Combined With 120-kV Equivalent Mixed Images. Front Oncol 2020;10:562945. [PMID: 33585186 DOI: 10.3389/fonc.2020.562945] [Reference Citation Analysis]
40 Mähringer‐kunz A, Wagner F, Hahn F, Weinmann A, Brodehl S, Schotten S, Hinrichs JB, Düber C, Galle PR, Pinto dos Santos D, Kloeckner R. Predicting survival after transarterial chemoembolization for hepatocellular carcinoma using a neural network: A Pilot Study. Liver Int 2019;40:694-703. [DOI: 10.1111/liv.14380] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
41 Lu J, Li B, Xiong X, Cheng N. RNA sequencing reveals the long noncoding RNA and mRNA profiles and identifies long non-coding RNA TSPAN12 as a potential microvascular invasion-related biomarker in hepatocellular carcinoma. Biomed Pharmacother 2020;126:110111. [PMID: 32222644 DOI: 10.1016/j.biopha.2020.110111] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
42 Harding-Theobald E, Louissaint J, Maraj B, Cuaresma E, Townsend W, Mendiratta-Lala M, Singal AG, Su GL, Lok AS, Parikh ND. Systematic review: radiomics for the diagnosis and prognosis of hepatocellular carcinoma. Aliment Pharmacol Ther 2021;54:890-901. [PMID: 34390014 DOI: 10.1111/apt.16563] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]
43 Liao H, Xiong T, Peng J, Xu L, Liao M, Zhang Z, Wu Z, Yuan K, Zeng Y. Classification and Prognosis Prediction from Histopathological Images of Hepatocellular Carcinoma by a Fully Automated Pipeline Based on Machine Learning. Ann Surg Oncol. 2020;27:2359-2369. [PMID: 31916093 DOI: 10.1245/s10434-019-08190-1] [Cited by in Crossref: 10] [Cited by in F6Publishing: 9] [Article Influence: 5.0] [Reference Citation Analysis]
44 Zhu D, Zhang M, Li Q, Liu J, Zhuang Y, Chen Q, Chen C, Xiang Y, Zhang Y, Yang Y. Can perihaematomal radiomics features predict haematoma expansion? Clin Radiol 2021;76:629.e1-9. [PMID: 33858695 DOI: 10.1016/j.crad.2021.03.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
45 Dana J, Venkatasamy A, Saviano A, Lupberger J, Hoshida Y, Vilgrain V, Nahon P, Reinhold C, Gallix B, Baumert TF. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease. Hepatol Int 2022. [PMID: 35138551 DOI: 10.1007/s12072-022-10303-0] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
46 Zhong X, Peng J, Xie Y, Shi Y, Long H, Su L, Duan Y, Xie X, Lin M. A nomogram based on multi-modal ultrasound for prediction of microvascular invasion and recurrence of hepatocellular carcinoma. Eur J Radiol 2022;151:110281. [PMID: 35395542 DOI: 10.1016/j.ejrad.2022.110281] [Reference Citation Analysis]
47 Yang Y, Zhou Y, Zhou C, Ma X. Deep learning radiomics based on contrast enhanced computed tomography predicts microvascular invasion and survival outcome in early stage hepatocellular carcinoma. Eur J Surg Oncol 2021:S0748-7983(21)00927-6. [PMID: 34862094 DOI: 10.1016/j.ejso.2021.11.120] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
48 Shi H, Duan Y, Shi J, Zhang W, Liu W, Shen B, Liu F, Mei X, Li X, Yuan Z. Role of preoperative prediction of microvascular invasion in hepatocellular carcinoma based on the texture of FDG PET image: A comparison of quantitative metabolic parameters and MRI. Front Physiol 2022;13:928969. [DOI: 10.3389/fphys.2022.928969] [Reference Citation Analysis]
49 Zhang R, Xu L, Wen X, Zhang J, Yang P, Zhang L, Xue X, Wang X, Huang Q, Guo C, Shi Y, Niu T, Chen F. A nomogram based on bi-regional radiomics features from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma.Quant Imaging Med Surg. 2019;9:1503-1515. [PMID: 31667137 DOI: 10.21037/qims.2019.09.07] [Cited by in Crossref: 17] [Cited by in F6Publishing: 21] [Article Influence: 5.7] [Reference Citation Analysis]
50 Zhang Y, Lv X, Qiu J, Zhang B, Zhang L, Fang J, Li M, Chen L, Wang F, Liu S, Zhang S. Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma. J Magn Reson Imaging 2021;54:134-43. [PMID: 33559293 DOI: 10.1002/jmri.27538] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
51 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]
52 Sun SW, Liu QP, Xu X, Zhu FP, Zhang YD, Liu XS. Direct Comparison of Four Presurgical Stratifying Schemes for Prediction of Microvascular Invasion in Hepatocellular Carcinoma by Gadoxetic Acid-Enhanced MRI. J Magn Reson Imaging 2020;52:433-47. [PMID: 31943465 DOI: 10.1002/jmri.27043] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
53 Zhang Y, Wei Q, Huang Y, Yao Z, Yan C, Zou X, Han J, Li Q, Mao R, Liao Y, Cao L, Lin M, Zhou X, Tang X, Hu Y, Li L, Wang Y, Yu J, Zhou J. Deep Learning of Liver Contrast-Enhanced Ultrasound to Predict Microvascular Invasion and Prognosis in Hepatocellular Carcinoma. Front Oncol 2022;12:878061. [DOI: 10.3389/fonc.2022.878061] [Reference Citation Analysis]
54 Xiao F, Sun R, Sun W, Xu D, Lan L, Li H, Liu H, Xu H. Radiomics analysis of chest CT to predict the overall survival for the severe patients of COVID-19 pneumonia. Phys Med Biol 2021;66. [PMID: 33845467 DOI: 10.1088/1361-6560/abf717] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
55 Li L, Su Q, Yang H. Preoperative prediction of microvascular invasion in hepatocellular carcinoma: a radiomic nomogram based on MRI. Clin Radiol 2021:S0009-9260(21)00576-6. [PMID: 34980458 DOI: 10.1016/j.crad.2021.12.008] [Reference Citation Analysis]
56 Wang JC, Hou JY, Chen JC, Xiang CL, Mao XH, Yang B, Li Q, Liu QB, Chen J, Ye ZW, Peng W, Sun XQ, Chen MS, Zhou QF, Zhang YJ. Development and validation of prognostic nomograms for single large and huge hepatocellular carcinoma after curative resection. Eur J Cancer 2021;155:85-96. [PMID: 34371445 DOI: 10.1016/j.ejca.2021.07.009] [Reference Citation Analysis]
57 Chen DS, Wang TF, Zhu JW, Zhu B, Wang ZL, Cao JG, Feng CH, Zhao JW. A Novel Application of Unsupervised Machine Learning and Supervised Machine Learning-Derived Radiomics in Anterior Cruciate Ligament Rupture. Risk Manag Healthc Policy 2021;14:2657-64. [PMID: 34188576 DOI: 10.2147/RMHP.S312330] [Reference Citation Analysis]
58 Zheng X, Wang P, Li L, Yu J, Yu C, Xu L, Li L, Dai F, Feng L, Zou H, Chen X, Zhang M, Xu M. Cancer-Associated Fibroblasts Promote Vascular Invasion of Hepatocellular Carcinoma via Downregulating Decorin-integrin β1 Signaling. Front Cell Dev Biol 2021;9:678670. [PMID: 34504839 DOI: 10.3389/fcell.2021.678670] [Reference Citation Analysis]
59 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]
60 Xia F, Zhu P, Chen XP, Zhang BX, Zhang MY. Prognostic Analysis of Postoperative Survival for Ruptured Hepatocellular Carcinoma with or without Cirrhosis. J Oncol 2022;2022:7531452. [PMID: 35342424 DOI: 10.1155/2022/7531452] [Reference Citation Analysis]
61 Kuang Y, Li R, Jia P, Ye W, Zhou R, Zhu R, Wang J, Lin S, Pang P, Ji W. MRI-Based Radiomics: Nomograms predicting the short-term response after transcatheter arterial chemoembolization (TACE) in hepatocellular carcinoma patients with diameter less than 5 cm. Abdom Radiol (NY) 2021;46:3772-89. [PMID: 33713159 DOI: 10.1007/s00261-021-02992-2] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
62 Ren Q, Zhu P, Li C, Yan M, Liu S, Zheng C, Xia X. Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor. Front Bioeng Biotechnol 2022;10:872044. [DOI: 10.3389/fbioe.2022.872044] [Reference Citation Analysis]
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