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For: Yao Z, Dong Y, Wu G, Zhang Q, Yang D, Yu JH, Wang WP. Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images. BMC Cancer. 2018;18:1089. [PMID: 30419849 DOI: 10.1186/s12885-018-5003-4] [Cited by in Crossref: 33] [Cited by in F6Publishing: 32] [Article Influence: 8.3] [Reference Citation Analysis]
Number Citing Articles
1 Du Y, Jiao J, Ji C, Li M, Guo Y, Wang Y, Zhou J, Ren Y. Ultrasound-based radiomics technology in fetal lung texture analysis prediction of neonatal respiratory morbidity. Sci Rep 2022;12:12747. [PMID: 35882938 DOI: 10.1038/s41598-022-17129-8] [Reference Citation Analysis]
2 Peng C, Yang C, Yao J, Xu J, Wu J, Zhao J, Xu D. Multimodal Sonographic Appearance and Survival Outcomes of 69 Cases of Primary Thyroid Lymphoma Over 10 Years. J Ultrasound Med 2022. [PMID: 35673932 DOI: 10.1002/jum.16032] [Reference Citation Analysis]
3 Zhong X, Long H, Su L, Zheng R, Wang W, Duan Y, Hu H, Lin M, Xie X. Radiomics models for preoperative prediction of microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis. Abdom Radiol (NY) 2022;47:2071-88. [PMID: 35364684 DOI: 10.1007/s00261-022-03496-3] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
4 Xu C, Jiang D, Tan B, Shen C, Guo J. Preoperative diagnosis and prediction of microvascular invasion in hepatocellularcarcinoma by ultrasound elastography. BMC Med Imaging 2022;22:88. [PMID: 35562688 DOI: 10.1186/s12880-022-00819-0] [Reference Citation Analysis]
5 Wu YQ, Gao RZ, Lin P, Wen R, Li HY, Mou MY, Chen FH, Huang F, Zhou WJ, Yang H, He Y, Wu J. An endorectal ultrasound-based radiomics signature for preoperative prediction of lymphovascular invasion of rectal cancer. BMC Med Imaging 2022;22:84. [PMID: 35538520 DOI: 10.1186/s12880-022-00813-6] [Reference Citation Analysis]
6 Dadoun H, Rousseau AL, de Kerviler E, Correas JM, Tissier AM, Joujou F, Bodard S, Khezzane K, de Margerie-Mellon C, Delingette H, Ayache N. Deep Learning for the Detection, Localization, and Characterization of Focal Liver Lesions on Abdominal US Images. Radiol Artif Intell 2022;4:e210110. [PMID: 35652113 DOI: 10.1148/ryai.210110] [Reference Citation Analysis]
7 Li L, Wu C, Huang Y, Chen J, Ye D, Su Z. Radiomics for the Preoperative Evaluation of Microvascular Invasion in Hepatocellular Carcinoma: A Meta-Analysis. Front Oncol 2022;12:831996. [PMID: 35463303 DOI: 10.3389/fonc.2022.831996] [Reference Citation Analysis]
8 Wu J, Ding W, Wang Y, Liu S, Zhang X, Yang Q, Cai W, Yu X, Liu F, Kong D, Zhong H, Yu J, Liang P. Radiomics analysis of ultrasound to predict recurrence of hepatocellular carcinoma after microwave ablation. International Journal of Hyperthermia 2022;39:595-604. [DOI: 10.1080/02656736.2022.2062463] [Reference Citation Analysis]
9 Lysdahlgaard S. Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study. Radiography (Lond) 2022:S1078-8174(22)00049-9. [PMID: 35428570 DOI: 10.1016/j.radi.2022.03.015] [Reference Citation Analysis]
10 Zhang Y, Cui J, Wan W, Liu J. Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57. Comput Intell Neurosci 2022;2022:4081654. [PMID: 35321452 DOI: 10.1155/2022/4081654] [Reference Citation Analysis]
11 Yang G, Zhang Y, Yu T, Chen M, Chen P. Exploratory study on the predictive value of ultrasound radiomics for cervical tuberculous lymphadenitis. Clin Imaging 2022;86:61-6. [PMID: 35339803 DOI: 10.1016/j.clinimag.2022.03.005] [Reference Citation Analysis]
12 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: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
13 Li C, Qiao G, Li J, Qi L, Wei X, Zhang T, Li X, Deng S, Wei X, Ma W. An Ultrasonic-Based Radiomics Nomogram for Distinguishing Between Benign and Malignant Solid Renal Masses. Front Oncol 2022;12:847805. [DOI: 10.3389/fonc.2022.847805] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Granata V, Fusco R, Setola SV, Simonetti I, Cozzi D, Grazzini G, Grassi F, Belli A, Miele V, Izzo F, Petrillo A. An update on radiomics techniques in primary liver cancers. Infect Agent Cancer 2022;17:6. [PMID: 35246207 DOI: 10.1186/s13027-022-00422-6] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
15 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] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
16 Huang H, Ruan SM, Xian MF, Li MD, Cheng MQ, Li W, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Hu HT, Chen LD. Contrast-enhanced ultrasound-based ultrasomics score: a potential biomarker for predicting early recurrence of hepatocellular carcinoma after resection or ablation. Br J Radiol 2021;:20210748. [PMID: 34797687 DOI: 10.1259/bjr.20210748] [Reference Citation Analysis]
17 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] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
18 Lee S, Summers RM. Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen. Radiol Clin North Am 2021;59:987-1002. [PMID: 34689882 DOI: 10.1016/j.rcl.2021.07.001] [Reference Citation Analysis]
19 Cheng MQ, Xian MF, Tian WS, Li MD, Hu HT, Li W, Zhang JC, Huang Y, Xie XY, Lu MD, Kuang M, Wang W, Ruan SM, Chen LD. RGB Three-Channel SWE-Based Ultrasomics Model: Improving the Efficiency in Differentiating Focal Liver Lesions. Front Oncol 2021;11:704218. [PMID: 34646763 DOI: 10.3389/fonc.2021.704218] [Reference Citation Analysis]
20 Sun Z, Jin L, Zhang S, Duan S, Xing W, Hu S. Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features. J Xray Sci Technol 2021;29:675-86. [PMID: 34024809 DOI: 10.3233/XST-210888] [Reference Citation Analysis]
21 Granata V, Grassi R, Fusco R, Belli A, Cutolo C, Pradella S, Grazzini G, La Porta M, Brunese MC, De Muzio F, Ottaiano A, Avallone A, Izzo F, Petrillo A. Diagnostic evaluation and ablation treatments assessment in hepatocellular carcinoma. Infect Agent Cancer 2021;16:53. [PMID: 34281580 DOI: 10.1186/s13027-021-00393-0] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F; Special Interest Group (SIG) Artificial Intelligence and Liver Diseases; Italian Association for the Study of the Liver (AISF). The application of artificial intelligence in hepatology: A systematic review. Dig Liver Dis 2021:S1590-8658(21)00317-0. [PMID: 34266794 DOI: 10.1016/j.dld.2021.06.011] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
23 La Greca Saint-Esteven A, Vuong D, Tschanz F, van Timmeren JE, Dal Bello R, Waller V, Pruschy M, Guckenberger M, Tanadini-Lang S. Systematic Review on the Association of Radiomics with Tumor Biological Endpoints. Cancers (Basel) 2021;13:3015. [PMID: 34208595 DOI: 10.3390/cancers13123015] [Reference Citation Analysis]
24 Nie P, Wang N, Pang J, Yang G, Duan S, Chen J, Xu W. CT-Based Radiomics Nomogram: A Potential Tool for Differentiating Hepatocellular Adenoma From Hepatocellular Carcinoma in the Noncirrhotic Liver. Acad Radiol 2021;28:799-807. [PMID: 32386828 DOI: 10.1016/j.acra.2020.04.027] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
25 Shi K, Xiao W, Wu G, Xiao Y, Lei Y, Yu J, Gu Y. Temporal-Spatial Feature Extraction of DSA Video and Its Application in AVM Diagnosis. Front Neurol 2021;12:655523. [PMID: 34122304 DOI: 10.3389/fneur.2021.655523] [Reference Citation Analysis]
26 Dong Y, Qiu Y, Yang D, Yu L, Zuo D, Zhang Q, Tian X, Wang WP, Jung EM. Potential application of dynamic contrast enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma. Clin Hemorheol Microcirc 2021;77:461-9. [PMID: 33459703 DOI: 10.3233/CH-201085] [Cited by in Crossref: 8] [Cited by in F6Publishing: 2] [Article Influence: 8.0] [Reference Citation Analysis]
27 Liang H, Hu C, Lu J, Zhang T, Jiang J, Ding D, Du S, Duan S. Correlation of radiomic features on dynamic contrast-enhanced magnetic resonance with microvessel density in hepatocellular carcinoma based on different models. J Int Med Res 2021;49:300060521997586. [PMID: 33682491 DOI: 10.1177/0300060521997586] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Chiappa V, Interlenghi M, Salvatore C, Bertolina F, Bogani G, Ditto A, Martinelli F, Castiglioni I, Raspagliesi F. Using rADioMIcs and machine learning with ultrasonography for the differential diagnosis of myometRiAL tumors (the ADMIRAL pilot study). Radiomics and differential diagnosis of myometrial tumors. Gynecol Oncol 2021;161:838-44. [PMID: 33867144 DOI: 10.1016/j.ygyno.2021.04.004] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
29 Li W, Lv XZ, Zheng X, Ruan SM, Hu HT, Chen LD, Huang Y, Li X, Zhang CQ, Xie XY, Kuang M, Lu MD, Zhuang BW, Wang W. Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma. Front Oncol 2021;11:544979. [PMID: 33842303 DOI: 10.3389/fonc.2021.544979] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
30 Gao RZ, Wen R, Wen DY, Huang J, Qin H, Li X, Wang XR, He Y, Yang H. Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer. J Ultrasound Med 2021. [PMID: 33615528 DOI: 10.1002/jum.15659] [Reference Citation Analysis]
31 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]
32 Mao B, Ma J, Duan S, Xia Y, Tao Y, Zhang L. Preoperative classification of primary and metastatic liver cancer via machine learning-based ultrasound radiomics. Eur Radiol 2021;31:4576-86. [PMID: 33447862 DOI: 10.1007/s00330-020-07562-6] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
33 Lewis S, Hectors S, Taouli B. Radiomics of hepatocellular carcinoma. Abdom Radiol (NY) 2021;46:111-23. [PMID: 31925492 DOI: 10.1007/s00261-019-02378-5] [Cited by in Crossref: 21] [Cited by in F6Publishing: 19] [Article Influence: 21.0] [Reference Citation Analysis]
34 Yin R, Jiang M, Lv WZ, Jiang F, Li J, Hu B, Cui XW, Dietrich CF. Study Processes and Applications of Ultrasomics in Precision Medicine. Front Oncol 2020;10:1736. [PMID: 33014858 DOI: 10.3389/fonc.2020.01736] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
35 Tiwari KA, Raišutis R, Liutkus J, Valiukevičienė S. Diagnostics of Melanocytic Skin Tumours by a Combination of Ultrasonic, Dermatoscopic and Spectrophotometric Image Parameters. Diagnostics (Basel) 2020;10:E632. [PMID: 32858850 DOI: 10.3390/diagnostics10090632] [Cited by in Crossref: 5] [Cited by in F6Publishing: 2] [Article Influence: 2.5] [Reference Citation Analysis]
36 Ai Y, Zhu H, Xie C, Jin X. Radiomics in cervical cancer: Current applications and future potential. Critical Reviews in Oncology/Hematology 2020;152:102985. [DOI: 10.1016/j.critrevonc.2020.102985] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 4.5] [Reference Citation Analysis]
37 Sun ZQ, Hu SD, Li J, Wang T, Duan SF, Wang J. Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images. J Xray Sci Technol 2019;27:1021-31. [PMID: 31640109 DOI: 10.3233/XST-190574] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 2.5] [Reference Citation Analysis]
38 Chiappa V, Bogani G, Interlenghi M, Salvatore C, Bertolina F, Sarpietro G, Signorelli M, Castiglioni I, Raspagliesi F. The Adoption of Radiomics and machine learning improves the diagnostic processes of women with Ovarian MAsses (the AROMA pilot study). J Ultrasound. [DOI: 10.1007/s40477-020-00503-5] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
39 Wei J, Jiang H, Gu D, Niu M, Fu F, Han Y, Song B, Tian J. Radiomics in liver diseases: Current progress and future opportunities. Liver Int 2020;40:2050-63. [PMID: 32515148 DOI: 10.1111/liv.14555] [Cited by in Crossref: 33] [Cited by in F6Publishing: 29] [Article Influence: 16.5] [Reference Citation Analysis]
40 Liang ZN, Yang W. Advances in diagnostic application of ultrasomics in liver lesions. Shijie Huaren Xiaohua Zazhi 2020; 28(12): 460-466 [DOI: 10.11569/wcjd.v28.i12.460] [Reference Citation Analysis]
41 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]
42 Piñero F, Tanno M, Aballay Soteras G, Tisi Baña M, Dirchwolf M, Fassio E, Ruf A, Mengarelli S, Borzi S, Fernández N, Ridruejo E, Descalzi V, Anders M, Mazzolini G, Reggiardo V, Marciano S, Perazzo F, Spina JC, McCormack L, Maraschio M, Lagues C, Gadano A, Villamil F, Silva M, Cairo F, Ameigeiras B; Argentinean Association for the Study of Liver Diseases (A.A.E.E.H). Argentinian clinical practice guideline for surveillance, diagnosis, staging and treatment of hepatocellular carcinoma. Ann Hepatol 2020;19:546-69. [PMID: 32593747 DOI: 10.1016/j.aohep.2020.06.003] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 1.0] [Reference Citation Analysis]
43 Zhang X, Ruan S, Xiao W, Shao J, Tian W, Liu W, Zhang Z, Wan D, Huang J, Huang Q, Yang Y, Yang H, Ding Y, Liang W, Bai X, Liang T. Contrast-enhanced CT radiomics for preoperative evaluation of microvascular invasion in hepatocellular carcinoma: A two-center study.Clin Transl Med. 2020;10:e111. [PMID: 32567245 DOI: 10.1002/ctm2.111] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
44 Peng YT, Zhou CY, Lin P, Wen DY, Wang XD, Zhong XZ, Pan DH, Que Q, Li X, Chen L, He Y, Yang H. Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma. Acad Radiol 2020;27:785-97. [PMID: 31494003 DOI: 10.1016/j.acra.2019.07.029] [Cited by in Crossref: 23] [Cited by in F6Publishing: 17] [Article Influence: 11.5] [Reference Citation Analysis]
45 Piñero F, Dirchwolf M, Pessôa MG. Biomarkers in Hepatocellular Carcinoma: Diagnosis, Prognosis and Treatment Response Assessment. Cells 2020;9:E1370. [PMID: 32492896 DOI: 10.3390/cells9061370] [Cited by in Crossref: 12] [Cited by in F6Publishing: 18] [Article Influence: 6.0] [Reference Citation Analysis]
46 Yang Q, Wei J, Hao X, Kong D, Yu X, Jiang T, Xi J, Cai W, Luo Y, Jing X, Yang Y, Cheng Z, Wu J, Zhang H, Liao J, Zhou P, Song Y, Zhang Y, Han Z, Cheng W, Tang L, Liu F, Dou J, Zheng R, Yu J, Tian J, Liang P. Improving B-mode ultrasound diagnostic performance for focal liver lesions using deep learning: A multicentre study. EBioMedicine. 2020;56:102777. [PMID: 32485640 DOI: 10.1016/j.ebiom.2020.102777] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
47 Nie P, Yang G, Guo J, Chen J, Li X, Ji Q, Wu J, Cui J, Xu W. A CT-based radiomics nomogram for differentiation of focal nodular hyperplasia from hepatocellular carcinoma in the non-cirrhotic liver. Cancer Imaging. 2020;20:20. [PMID: 32093786 DOI: 10.1186/s40644-020-00297-z] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
48 Bing MM, Shaobo DM, Ruiqing LM, Na LP, Yaqiong LP, Lianzhong ZM. The Roles of Ultrasound-Based Radiomics In Precision Diagnosis and Treatment of Different Cancers: A Literature Review. Advanced Ultrasound in Diagnosis and Therapy 2020;4:291. [DOI: 10.37015/audt.2020.200051] [Reference Citation Analysis]
49 Dong Y, Wang QM, Li Q, Li LY, Zhang Q, Yao Z, Dai M, Yu J, Wang WP. Preoperative Prediction of Microvascular Invasion of Hepatocellular Carcinoma: Radiomics Algorithm Based on Ultrasound Original Radio Frequency Signals. Front Oncol 2019;9:1203. [PMID: 31799183 DOI: 10.3389/fonc.2019.01203] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
50 Dalal V, Carmicheal J, Dhaliwal A, Jain M, Kaur S, Batra SK. Radiomics in stratification of pancreatic cystic lesions: Machine learning in action. Cancer Lett 2020;469:228-37. [PMID: 31629933 DOI: 10.1016/j.canlet.2019.10.023] [Cited by in Crossref: 20] [Cited by in F6Publishing: 15] [Article Influence: 6.7] [Reference Citation Analysis]
51 Weng Q, Zhou L, Wang H, Hui J, Chen M, Pang P, Zheng L, Xu M, Wang Z, Ji J. A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Clin Radiol 2019;74:933-43. [PMID: 31521324 DOI: 10.1016/j.crad.2019.07.026] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
52 Balaceanu LA. Biomarkers vs imaging in the early detection of hepatocellular carcinoma and prognosis. World J Clin Cases 2019; 7(12): 1367-1382 [PMID: 31363465 DOI: 10.12998/wjcc.v7.i12.1367] [Cited by in CrossRef: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]