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For: Fowler KJ, Tang A, Santillan C, Bhargavan-Chatfield M, Heiken J, Jha RC, Weinreb J, Hussain H, Mitchell DG, Bashir MR, Costa EAC, Cunha GM, Coombs L, Wolfson T, Gamst AC, Brancatelli G, Yeh B, Sirlin CB. Interreader Reliability of LI-RADS Version 2014 Algorithm and Imaging Features for Diagnosis of Hepatocellular Carcinoma: A Large International Multireader Study. Radiology. 2018;286:173-185. [PMID: 29091751 DOI: 10.1148/radiol.2017170376] [Cited by in Crossref: 51] [Cited by in F6Publishing: 48] [Article Influence: 10.2] [Reference Citation Analysis]
Number Citing Articles
1 Alhasan A, Cerny M, Olivié D, Billiard J, Bergeron C, Brown K, Bodson-clermont P, Castel H, Turcotte S, Perreault P, Tang A. LI-RADS for CT diagnosis of hepatocellular carcinoma: performance of major and ancillary features. Abdom Radiol 2019;44:517-28. [DOI: 10.1007/s00261-018-1762-2] [Cited by in Crossref: 12] [Cited by in F6Publishing: 12] [Article Influence: 3.0] [Reference Citation Analysis]
2 Zhang N, Xu H, Ren AH, Zhang Q, Yang DW, Ba T, Wang ZC, Yang ZH. Does Training in LI-RADS Version 2018 Improve Readers' Agreement with the Expert Consensus and Inter-reader Agreement in MRI Interpretation? J Magn Reson Imaging 2021. [PMID: 33963801 DOI: 10.1002/jmri.27688] [Reference Citation Analysis]
3 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] [Reference Citation Analysis]
4 Yokoo T, Singal AG, Diaz de Leon A, Ananthakrishnan L, Fetzer DT, Pedrosa I, Khatri G. Prevalence and clinical significance of discordant LI-RADS® observations on multiphase contrast-enhanced MRI in patients with cirrhosis. Abdom Radiol 2020;45:177-87. [DOI: 10.1007/s00261-019-02133-w] [Cited by in Crossref: 5] [Cited by in F6Publishing: 3] [Article Influence: 1.7] [Reference Citation Analysis]
5 Elsayes KM, Fowler KJ, Chernyak V, Elmohr MM, Kielar AZ, Hecht E, Bashir MR, Furlan A, Sirlin CB. User and system pitfalls in liver imaging with LI-RADS. J Magn Reson Imaging 2019;50:1673-86. [PMID: 31215119 DOI: 10.1002/jmri.26839] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
6 Ludwig DR, Romberg EK, Fraum TJ, Rohe E, Fowler KJ, Khanna G. Diagnostic performance of Liver Imaging Reporting and Data System (LI-RADS) v2017 in predicting malignant liver lesions in pediatric patients: a preliminary study. Pediatr Radiol 2019;49:746-58. [DOI: 10.1007/s00247-019-04358-9] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 2.7] [Reference Citation Analysis]
7 Furlan A, Almusa O, Yu RK, Sagreiya H, Borhani AA, Bae KT, Marsh JW. A radiogenomic analysis of hepatocellular carcinoma: association between fractional allelic imbalance rate index and the liver imaging reporting and data system (LI-RADS) categories and features. Br J Radiol 2018;91:20170962. [PMID: 29565672 DOI: 10.1259/bjr.20170962] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 0.8] [Reference Citation Analysis]
8 Cools KS, Moon AM, Burke LMB, McGinty KA, Strassle PD, Gerber DA. Validation of the Liver Imaging Reporting and Data System Treatment Response Criteria After Thermal Ablation for Hepatocellular Carcinoma. Liver Transpl 2020;26:203-14. [PMID: 31677319 DOI: 10.1002/lt.25673] [Cited by in Crossref: 15] [Cited by in F6Publishing: 15] [Article Influence: 5.0] [Reference Citation Analysis]
9 Kim YY, Choi JY, Sirlin CB, An C, Kim MJ. Pitfalls and problems to be solved in the diagnostic CT/MRI Liver Imaging Reporting and Data System (LI-RADS). Eur Radiol 2019;29:1124-32. [PMID: 30116960 DOI: 10.1007/s00330-018-5641-6] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 4.0] [Reference Citation Analysis]
10 Khatri G, Pedrosa I, Ananthakrishnan L, Leon AD, Fetzer DT, Leyendecker J, Singal AG, Xi Y, Yopp A, Yokoo T. Abbreviated‐protocol screening MRI vs. complete‐protocol diagnostic MRI for detection of hepatocellular carcinoma in patients with cirrhosis: An equivalence study using LI‐RADS v2018. J Magn Reson Imaging 2019;51:415-25. [DOI: 10.1002/jmri.26835] [Cited by in Crossref: 22] [Cited by in F6Publishing: 21] [Article Influence: 7.3] [Reference Citation Analysis]
11 Cannella R, Vernuccio F, Antonucci M, Gagliano DS, Matteini F, Midiri M, Brancatelli G. LI-RADS ancillary features favoring benignity: is there a role in LR-5 observations? Eur Radiol 2021. [PMID: 34545444 DOI: 10.1007/s00330-021-08267-0] [Reference Citation Analysis]
12 Byrd K, Alqahtani S, Yopp AC, Singal AG. Role of Multidisciplinary Care in the Management of Hepatocellular Carcinoma. Semin Liver Dis 2021;41:1-8. [PMID: 33764480 DOI: 10.1055/s-0040-1719178] [Reference Citation Analysis]
13 Chan A, Sertic M, Sammon J, Kim TK, Jang HJ, Guimaraes L, O'Malley M, Khalili K. Diagnostic imaging of hepatocellular carcinoma at community hospitals and their tertiary referral center in the era of LI-RADS: a quality assessment study. Abdom Radiol (NY) 2019;44:4028-36. [PMID: 31555846 DOI: 10.1007/s00261-019-02237-3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
14 Yamashita R, Mittendorf A, Zhu Z, Fowler KJ, Santillan CS, Sirlin CB, Bashir MR, Do RKG. Deep convolutional neural network applied to the liver imaging reporting and data system (LI-RADS) version 2014 category classification: a pilot study. Abdom Radiol45:24-35. [PMID: 31696269 DOI: 10.1007/s00261-019-02306-7] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 9.0] [Reference Citation Analysis]
15 Li J, Chen M, Wang ZJ, Li SG, Jiang M, Shi L, Cao CL, Sang T, Cui XW, Dietrich CF. Interobserver agreement for contrast-enhanced ultrasound of liver imaging reporting and data system: A systematic review and meta-analysis. World J Clin Cases 2020; 8(22): 5589-5602 [PMID: 33344549 DOI: 10.12998/wjcc.v8.i22.5589] [Cited by in CrossRef: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
16 Kang JH, Choi SH, Lee JS, Park SH, Kim KW, Kim SY, Lee SS, Byun JH. Interreader Agreement of Liver Imaging Reporting and Data System on MRI: A Systematic Review and Meta‐Analysis. J Magn Reson Imaging 2020;52:795-804. [DOI: 10.1002/jmri.27065] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 5.5] [Reference Citation Analysis]
17 Masch WR, Parikh ND, Licari TL, Mendiratta-Lala M, Davenport MS. Radiologist Quality Assurance by Nonradiologists at Tumor Board. J Am Coll Radiol 2018;15:1259-65. [PMID: 29866627 DOI: 10.1016/j.jacr.2018.04.021] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
18 Jeon SK, Joo I, Lee DH, Lee SM, Kang HJ, Lee KB, Lee JM. Combined hepatocellular cholangiocarcinoma: LI-RADS v2017 categorisation for differential diagnosis and prognostication on gadoxetic acid-enhanced MR imaging. Eur Radiol. 2019;29:373-382. [PMID: 29955948 DOI: 10.1007/s00330-018-5605-x] [Cited by in Crossref: 53] [Cited by in F6Publishing: 52] [Article Influence: 13.3] [Reference Citation Analysis]
19 Yacoub JH, Elsayes KM, Fowler KJ, Hecht EM, Mitchell DG, Santillan C, Szklaruk J. Pitfalls in liver MRI: Technical approach to avoiding misdiagnosis and improving image quality: Pitfalls in Liver MRI: Technical Approach. J Magn Reson Imaging 2019;49:41-58. [DOI: 10.1002/jmri.26343] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 2.5] [Reference Citation Analysis]
20 Lee SM, Lee JM, Ahn SJ, Kang HJ, Yang HK, Yoon JH. Diagnostic Performance of 2018 KLCA-NCC Practice Guideline for Hepatocellular Carcinoma on Gadoxetic Acid-Enhanced MRI in Patients with Chronic Hepatitis B or Cirrhosis: Comparison with LI-RADS Version 2018. Korean J Radiol 2021;22:1066-76. [PMID: 33739633 DOI: 10.3348/kjr.2020.0846] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
21 van der Pol CB, Dhindsa K, Shergill R, Zha N, Ferri M, Kagoma YK, Lee SY, Satkunasingham J, Wat J, Tsai S. MRI LI-RADS Version 2018: Impact of and Reduction in Ancillary Features. AJR Am J Roentgenol 2021;216:935-42. [PMID: 33534620 DOI: 10.2214/AJR.20.23031] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
22 Chernyak V, Flusberg M, Berman J, Fruitman KC, Kobi M, Fowler KJ, Sirlin CB. Liver Imaging Reporting and Data System Version 2018: Impact on Categorization and Hepatocellular Carcinoma Staging. Liver Transpl 2019;25:1488-502. [DOI: 10.1002/lt.25614] [Cited by in Crossref: 12] [Cited by in F6Publishing: 7] [Article Influence: 4.0] [Reference Citation Analysis]
23 Park SH, Shim YS, Kim B, Kim SY, Kim YS, Huh J, Park JH, Kim KW, Lee SS. Retrospective analysis of current guidelines for hepatocellular carcinoma diagnosis on gadoxetic acid-enhanced MRI in at-risk patients. Eur Radiol 2021;31:4751-63. [PMID: 33389037 DOI: 10.1007/s00330-020-07577-z] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
24 Vernuccio F, Cannella R, Choudhury KR, Meyer M, Furlan A, Marin D. Hepatobiliary phase hypointensity predicts progression to hepatocellular carcinoma for intermediate-high risk observations, but not time to progression. Eur J Radiol 2020;128:109018. [PMID: 32388318 DOI: 10.1016/j.ejrad.2020.109018] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
25 Ren AH, Xu H, Yang DW, Zhang N, Ba T, Wang ZC, Yang ZH. Systematic Training of Liver Imaging Reporting and Data System Magnetic Resonance Imaging v2018 can Improve the Diagnosis of Hepatocellular Carcinoma for Different Radiologists. J Clin Transl Hepatol 2021;9:537-44. [PMID: 34447683 DOI: 10.14218/JCTH.2021.00180] [Reference Citation Analysis]
26 Elsayes KM, Kielar AZ, Chernyak V, Morshid A, Furlan A, Masch WR, Marks RM, Kamaya A, Do RKG, Kono Y, Fowler KJ, Tang A, Bashir MR, Hecht EM, Jambhekar K, Lyshchik A, Rodgers SK, Heiken JP, Kohli M, Fetzer DT, Wilson SR, Kassam Z, Mendiratta-Lala M, Singal AG, Lim CS, Cruite I, Lee J, Ash R, Mitchell DG, McInnes MDF, Sirlin CB. LI-RADS: a conceptual and historical review from its beginning to its recent integration into AASLD clinical practice guidance.J Hepatocell Carcinoma. 2019;6:49-69. [PMID: 30788336 DOI: 10.2147/JHC.S186239] [Cited by in Crossref: 42] [Cited by in F6Publishing: 13] [Article Influence: 14.0] [Reference Citation Analysis]
27 Stocker D, Becker AS, Barth BK, Skawran S, Kaniewska M, Fischer MA, Donati O, Reiner CS. Does quantitative assessment of arterial phase hyperenhancement and washout improve LI-RADS v2018–based classification of liver lesions? Eur Radiol 2020;30:2922-33. [DOI: 10.1007/s00330-019-06596-9] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
28 Cannella R, Ronot M, Sartoris R, Cauchy F, Hobeika C, Beaufrere A, Trapani L, Paradis V, Bouattour M, Bonvalet F, Vilgrain V, Dioguardi Burgio M. Enhancing capsule in hepatocellular carcinoma: intra-individual comparison between CT and MRI with extracellular contrast agent. Diagn Interv Imaging 2021:S2211-5684(21)00166-2. [PMID: 34284951 DOI: 10.1016/j.diii.2021.06.004] [Reference Citation Analysis]
29 Vernuccio F, Cannella R, Meyer M, Choudhoury KR, Gonzáles F, Schwartz FR, Gupta RT, Bashir MR, Furlan A, Marin D. LI-RADS: Diagnostic Performance of Hepatobiliary Phase Hypointensity and Major Imaging Features of LR-3 and LR-4 Lesions Measuring 10-19 mm With Arterial Phase Hyperenhancement. AJR Am J Roentgenol 2019;213:W57-65. [PMID: 31039012 DOI: 10.2214/AJR.18.20979] [Cited by in Crossref: 13] [Cited by in F6Publishing: 5] [Article Influence: 4.3] [Reference Citation Analysis]
30 Ludwig DR, Fraum TJ, Cannella R, Tsai R, Naeem M, LeBlanc M, Salter A, Tsung A, Fleckenstein J, Shetty AS, Borhani AA, Furlan A, Fowler KJ. Expanding the Liver Imaging Reporting and Data System (LI-RADS) v2018 diagnostic population: performance and reliability of LI-RADS for distinguishing hepatocellular carcinoma (HCC) from non-HCC primary liver carcinoma in patients who do not meet strict LI-RADS high-risk criteria. HPB (Oxford) 2019;21:1697-706. [PMID: 31262487 DOI: 10.1016/j.hpb.2019.04.007] [Cited by in Crossref: 7] [Cited by in F6Publishing: 7] [Article Influence: 2.3] [Reference Citation Analysis]
31 Abdel Razek AAK, El-Serougy LG, Saleh GA, Abd El-Wahab R, Shabana W. Interobserver Agreement of Magnetic Resonance Imaging of Liver Imaging Reporting and Data System Version 2018. J Comput Assist Tomogr. 2020;44:118-123. [PMID: 31939892 DOI: 10.1097/rct.0000000000000945] [Cited by in Crossref: 18] [Cited by in F6Publishing: 5] [Article Influence: 9.0] [Reference Citation Analysis]
32 Hamm CA, Wang CJ, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Duncan JS, Weinreb JC, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part I: development of a convolutional neural network classifier for multi-phasic MRI. Eur Radiol. 2019;29:3338-3347. [PMID: 31016442 DOI: 10.1007/s00330-019-06205-9] [Cited by in Crossref: 66] [Cited by in F6Publishing: 56] [Article Influence: 22.0] [Reference Citation Analysis]
33 Ludwig DR, Fraum TJ, Cannella R, Ballard DH, Tsai R, Naeem M, LeBlanc M, Salter A, Tsung A, Shetty AS, Borhani AA, Furlan A, Fowler KJ. Hepatocellular carcinoma (HCC) versus non-HCC: accuracy and reliability of Liver Imaging Reporting and Data System v2018. Abdom Radiol (NY) 2019;44:2116-32. [PMID: 30798397 DOI: 10.1007/s00261-019-01948-x] [Cited by in Crossref: 32] [Cited by in F6Publishing: 27] [Article Influence: 16.0] [Reference Citation Analysis]
34 Bashir MR, Chernyak V. Can MRI Features of Combined Hepatocellular Carcinoma-Intrahepatic Cholangiogarcinoma Help Predict Tumor Behavior Better than Histologic Findings? Radiology 2019;290:398-9. [PMID: 30422087 DOI: 10.1148/radiol.2018182408] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
35 Wang CJ, Hamm CA, Savic LJ, Ferrante M, Schobert I, Schlachter T, Lin M, Weinreb JC, Duncan JS, Chapiro J, Letzen B. Deep learning for liver tumor diagnosis part II: convolutional neural network interpretation using radiologic imaging features. Eur Radiol. 2019;29:3348-3357. [PMID: 31093705 DOI: 10.1007/s00330-019-06214-8] [Cited by in Crossref: 30] [Cited by in F6Publishing: 27] [Article Influence: 10.0] [Reference Citation Analysis]
36 Hwang SH, Park S, Han K, Choi J, Park Y, Park M. Optimal lexicon of gadoxetic acid-enhanced magnetic resonance imaging for the diagnosis of hepatocellular carcinoma modified from LI-RADS. Abdom Radiol 2019;44:3078-88. [DOI: 10.1007/s00261-019-02077-1] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 2.3] [Reference Citation Analysis]
37 Puttagunta S, van der Pol CB, Ferri M, Sy Wat J, Kulkarni A, Carrion-Martinez I. Diagnostic Accuracy of Single-Phase Computed Tomography Texture Analysis for Prediction of LI-RADS v2018 Category. J Comput Assist Tomogr 2020;44:188-92. [PMID: 32195797 DOI: 10.1097/RCT.0000000000001003] [Reference Citation Analysis]
38 Kang JH, Choi SH, Lee JS, Kim KW, Kim SY, Lee SS, Byun JH. Inter-reader reliability of CT Liver Imaging Reporting and Data System according to imaging analysis methodology: a systematic review and meta-analysis. Eur Radiol 2021;31:6856-67. [PMID: 33713172 DOI: 10.1007/s00330-021-07815-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
39 Sheng R, Huang J, Zhang W, Jin K, Yang L, Chong H, Fan J, Zhou J, Wu D, Zeng M. A Semi-Automatic Step-by-Step Expert-Guided LI-RADS Grading System Based on Gadoxetic Acid-Enhanced MRI. J Hepatocell Carcinoma 2021;8:671-83. [PMID: 34235105 DOI: 10.2147/JHC.S316385] [Reference Citation Analysis]
40 Chernyak V, Fowler KJ, Kamaya A, Kielar AZ, Elsayes KM, Bashir MR, Kono Y, Do RK, Mitchell DG, Singal AG, Tang A, Sirlin CB. Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology 2018;289:816-30. [PMID: 30251931 DOI: 10.1148/radiol.2018181494] [Cited by in Crossref: 212] [Cited by in F6Publishing: 196] [Article Influence: 53.0] [Reference Citation Analysis]
41 Hong CW, Park CC, Mamidipalli A, Hooker JC, Fazeli Dehkordy S, Igarashi S, Alhumayed M, Kono Y, Loomba R, Wolfson T, Gamst A, Murphy P, Sirlin CB. Longitudinal evolution of CT and MRI LI-RADS v2014 category 1, 2, 3, and 4 observations. Eur Radiol 2019;29:5073-81. [PMID: 30809719 DOI: 10.1007/s00330-019-06058-2] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 2.7] [Reference Citation Analysis]
42 An C, Lee CH, Byun JH, Lee MH, Jeong WK, Choi SH, Kim DY, Lim YS, Kim YS, Kim JH, Choi MS, Kim MJ. Intraindividual Comparison between Gadoxetate-Enhanced Magnetic Resonance Imaging and Dynamic Computed Tomography for Characterizing Focal Hepatic Lesions: A Multicenter, Multireader Study. Korean J Radiol 2019;20:1616-26. [PMID: 31854149 DOI: 10.3348/kjr.2019.0363] [Cited by in Crossref: 10] [Cited by in F6Publishing: 8] [Article Influence: 5.0] [Reference Citation Analysis]
43 Sevim S, Dicle O, Gezer NS, Barış MM, Altay C, Akın IB. How high is the inter-observer reproducibility in the LIRADS reporting system? Pol J Radiol 2019;84:e464-9. [PMID: 31969967 DOI: 10.5114/pjr.2019.90090] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 0.7] [Reference Citation Analysis]
44 Ko A, Park HJ, Lee ES, Park SB, Kim YK, Choi SY, Ahn S. Comparison of the diagnostic performance of the 2017 and 2018 versions of LI-RADS for hepatocellular carcinoma on gadoxetic acid enhanced MRI. Clin Radiol 2020;75:319.e1-9. [PMID: 31858990 DOI: 10.1016/j.crad.2019.11.004] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
45 Cannella R, Vernuccio F, Sagreiya H, Choudhury KR, Iranpour N, Marin D, Furlan A. Liver Imaging Reporting and Data System (LI-RADS) v2018: diagnostic value of ancillary features favoring malignancy in hypervascular observations ≥ 10 mm at intermediate (LR-3) and high probability (LR-4) for hepatocellular carcinoma. Eur Radiol 2020;30:3770-81. [PMID: 32107603 DOI: 10.1007/s00330-020-06698-9] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
46 Ballotin VR, Bigarella LG, Soldera J, Soldera J. Deep learning applied to the imaging diagnosis of hepatocellular carcinoma. Artif Intell Gastrointest Endosc 2021; 2(4): 127-135 [DOI: 10.37126/aige.v2.i4.127] [Reference Citation Analysis]
47 Ren A, Du J, Yang D, Zhao P, Wang Z, Yang Z. The role of ancillary features for diagnosing hepatocellular carcinoma on CT: based on the Liver Imaging Reporting and Data System version 2017 algorithm. Clinical Radiology 2020;75:478.e25-35. [DOI: 10.1016/j.crad.2019.08.031] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
48 Piñero F, Thompson MA, Diaz Telli F, Trentacoste J, Padín C, Mendizabal M, Colaci C, Gonzalez Campaña A, Pages J, Montal S, Barreiro M, Fauda M, Podestá G, Perotti JP, Silva M. LI-RADS 4 or 5 categorization may not be clinically relevant for decision-making processes: A prospective cohort study. Ann Hepatol 2020;19:662-7. [PMID: 32683095 DOI: 10.1016/j.aohep.2020.06.007] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
49 Cannella R, Fowler KJ, Borhani AA, Minervini MI, Heller M, Furlan A. Common pitfalls when using the Liver Imaging Reporting and Data System (LI-RADS): lessons learned from a multi-year experience. Abdom Radiol 2019;44:43-53. [DOI: 10.1007/s00261-018-1720-z] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
50 Abdelaziz TT, Abdel Razk AAK, Ashour MMM, Abdelrahman AS. Interreader reproducibility of the Neck Imaging Reporting and Data system (NI-RADS) lexicon for the detection of residual/recurrent disease in treated head and neck squamous cell carcinoma (HNSCC). Cancer Imaging 2020;20:61. [PMID: 32811559 DOI: 10.1186/s40644-020-00337-8] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]