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For: Acs B, Rantalainen M, Hartman J. Artificial intelligence as the next step towards precision pathology. J Intern Med. 2020;288:62-81. [PMID: 32128929 DOI: 10.1111/joim.13030] [Cited by in Crossref: 46] [Cited by in F6Publishing: 44] [Article Influence: 23.0] [Reference Citation Analysis]
Number Citing Articles
1 Hou J, Nast CC. Artificial Intelligence: The Next Frontier in Kidney Biopsy Evaluation. Clin J Am Soc Nephrol 2020;15:1389-91. [PMID: 32938618 DOI: 10.2215/CJN.13450820] [Reference Citation Analysis]
2 Wang Y, He X, Nie H, Zhou J, Cao P, Ou C. Application of artificial intelligence to the diagnosis and therapy of colorectal cancer. Am J Cancer Res. 2020;10:3575-3598. [PMID: 33294256 DOI: 10.7150/thno.49168] [Cited by in Crossref: 10] [Cited by in F6Publishing: 14] [Article Influence: 5.0] [Reference Citation Analysis]
3 Chelebian E, Avenel C, Kartasalo K, Marklund M, Tanoglidi A, Mirtti T, Colling R, Erickson A, Lamb AD, Lundeberg J, Wählby C. Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer. Cancers (Basel) 2021;13:4837. [PMID: 34638322 DOI: 10.3390/cancers13194837] [Reference Citation Analysis]
4 Vale-Silva LA, Rohr K. Long-term cancer survival prediction using multimodal deep learning. Sci Rep 2021;11:13505. [PMID: 34188098 DOI: 10.1038/s41598-021-92799-4] [Reference Citation Analysis]
5 Schneider L, Laiouar-Pedari S, Kuntz S, Krieghoff-Henning E, Hekler A, Kather JN, Gaiser T, Fröhling S, Brinker TJ. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur J Cancer 2022;160:80-91. [PMID: 34810047 DOI: 10.1016/j.ejca.2021.10.007] [Reference Citation Analysis]
6 Malherbe K. Tumor Microenvironment and the Role of Artificial Intelligence in Breast Cancer Detection and Prognosis. Am J Pathol 2021;191:1364-73. [PMID: 33639101 DOI: 10.1016/j.ajpath.2021.01.014] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
7 Oliveira SP, Neto PC, Fraga J, Montezuma D, Monteiro A, Monteiro J, Ribeiro L, Gonçalves S, Pinto IM, Cardoso JS. CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance. Sci Rep 2021;11:14358. [PMID: 34257363 DOI: 10.1038/s41598-021-93746-z] [Reference Citation Analysis]
8 Li LR, Du B, Liu HQ, Chen C. Artificial Intelligence for Personalized Medicine in Thyroid Cancer: Current Status and Future Perspectives. Front Oncol 2020;10:604051. [PMID: 33634025 DOI: 10.3389/fonc.2020.604051] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
9 Reid J, Parmar P, Lund T, Aalto DK, Jeffery CC. Development of a machine-learning based voice disorder screening tool. Am J Otolaryngol 2021;43:103327. [PMID: 34923280 DOI: 10.1016/j.amjoto.2021.103327] [Reference Citation Analysis]
10 Moran-Sanchez J, Santisteban-Espejo A, Martin-Piedra MA, Perez-Requena J, Garcia-Rojo M. Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis. Biomolecules 2021;11:793. [PMID: 34070632 DOI: 10.3390/biom11060793] [Reference Citation Analysis]
11 Liang F, Wang S, Zhang K, Liu TJ, Li JN. Development of artificial intelligence technology in diagnosis, treatment, and prognosis of colorectal cancer. World J Gastrointest Oncol 2022; 14(1): 124-152 [DOI: 10.4251/wjgo.v14.i1.124] [Reference Citation Analysis]
12 Kazdal D, Hofman V, Christopoulos P, Ilié M, Stenzinger A, Hofman P. Fusion-positive non-small cell lung carcinoma: Biological principles, clinical practice and diagnostic implications. Genes Chromosomes Cancer 2022. [PMID: 34997651 DOI: 10.1002/gcc.23022] [Reference Citation Analysis]
13 Taylor-Weiner A, Pokkalla H, Han L, Jia C, Huss R, Chung C, Elliott H, Glass B, Pethia K, Carrasco-Zevallos O, Shukla C, Khettry U, Najarian R, Taliano R, Subramanian GM, Myers RP, Wapinski I, Khosla A, Resnick M, Montalto MC, Anstee QM, Wong VW, Trauner M, Lawitz EJ, Harrison SA, Okanoue T, Romero-Gomez M, Goodman Z, Loomba R, Beck AH, Younossi ZM. A Machine Learning Approach Enables Quantitative Measurement of Liver Histology and Disease Monitoring in NASH. Hepatology 2021;74:133-47. [PMID: 33570776 DOI: 10.1002/hep.31750] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
14 Lagree A, Shiner A, Alera MA, Fleshner L, Law E, Law B, Lu FI, Dodington D, Gandhi S, Slodkowska EA, Shenfield A, Jerzak KJ, Sadeghi-Naini A, Tran WT. Assessment of Digital Pathology Imaging Biomarkers Associated with Breast Cancer Histologic Grade. Curr Oncol 2021;28:4298-316. [PMID: 34898544 DOI: 10.3390/curroncol28060366] [Reference Citation Analysis]
15 Liu PR, Lu L, Zhang JY, Huo TT, Liu SX, Ye ZW. Application of Artificial Intelligence in Medicine: An Overview. Curr Med Sci 2021. [PMID: 34874486 DOI: 10.1007/s11596-021-2474-3] [Reference Citation Analysis]
16 Xu F, Zhu C, Tang W, Wang Y, Zhang Y, Li J, Jiang H, Shi Z, Liu J, Jin M. Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides. Front Oncol 2021;11:759007. [PMID: 34722313 DOI: 10.3389/fonc.2021.759007] [Reference Citation Analysis]
17 Jasu J, Tolonen T, Antonarakis ES, Beltran H, Halabi S, Eisenberger MA, Carducci MA, Loriot Y, Van der Eecken K, Lolkema M, Ryan CJ, Taavitsainen S, Gillessen S, Högnäs G, Talvitie T, Taylor RJ, Koskenalho A, Ost P, Murtola TJ, Rinta-Kiikka I, Tammela T, Auvinen A, Kujala P, Smith TJ, Kellokumpu-Lehtinen PL, Isaacs WB, Nykter M, Kesseli J, Bova GS. Combined Longitudinal Clinical and Autopsy Phenomic Assessment in Lethal Metastatic Prostate Cancer: Recommendations for Advancing Precision Medicine. Eur Urol Open Sci 2021;30:47-62. [PMID: 34337548 DOI: 10.1016/j.euros.2021.05.011] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 Yee NS. Machine intelligence for precision oncology. World J Transl Med 2021; 9(1): 1-10 [DOI: 10.5528/wjtm.v9.i1.1] [Reference Citation Analysis]
19 Wang Y, Acs B, Robertson S, Liu B, Solorzano L, Wählby C, Hartman J, Rantalainen M. Improved breast cancer histological grading using deep learning. Ann Oncol 2021:S0923-7534(21)04486-0. [PMID: 34756513 DOI: 10.1016/j.annonc.2021.09.007] [Reference Citation Analysis]
20 Müller H, Holzinger A, Plass M, Brcic L, Stumptner C, Zatloukal K. Explainability and Causability for Artificial Intelligence-Supported Medical Image Analysis in the Context of the European In Vitro Diagnostic Regulation. New Biotechnology 2022. [DOI: 10.1016/j.nbt.2022.05.002] [Reference Citation Analysis]
21 Lu Y, Gao X, Venkateswaran N. The Impact of Artificial Intelligence Technology on Market Public Administration in a Complex Market Environment. Wireless Communications and Mobile Computing 2022;2022:1-13. [DOI: 10.1155/2022/5646234] [Reference Citation Analysis]
22 Pollett A. Colorectal Cancer: Microsatellite Instability/Mismatch Repair Testing in the Era of Digital Pathology. Gastroenterology 2020;159:1235-7. [PMID: 32800777 DOI: 10.1053/j.gastro.2020.08.008] [Reference Citation Analysis]
23 Jheng YC, Kao CL, Yarmishyn AA, Chou YB, Hsu CC, Lin TC, Hu HK, Ho TK, Chen PY, Kao ZK, Chen SJ, Hwang DK. The era of artificial intelligence-based individualized telemedicine is coming. J Chin Med Assoc 2020;83:981-3. [PMID: 32568967 DOI: 10.1097/JCMA.0000000000000374] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
24 Kudou M, Kosuga T, Otsuji E. Artificial intelligence in gastrointestinal cancer: Recent advances and future perspectives. Artif Intell Gastroenterol 2020; 1(4): 71-85 [DOI: 10.35712/aig.v1.i4.71] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
25 Choi S, Cho SI, Ma M, Park S, Pereira S, Aum BJ, Shin S, Paeng K, Yoo D, Jung W, Ock C, Lee S, Choi Y, Chung J, Mok TS, Kim H, Kim S. Artificial intelligence–powered programmed death ligand 1 analyser reduces interobserver variation in tumour proportion score for non–small cell lung cancer with better prediction of immunotherapy response. European Journal of Cancer 2022;170:17-26. [DOI: 10.1016/j.ejca.2022.04.011] [Reference Citation Analysis]
26 Schwen LO, Schacherer D, Geißler C, Homeyer A. Evaluating generic AutoML tools for computational pathology. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.100853] [Reference Citation Analysis]
27 Krause SW. On Its Way to Primetime: Artificial Intelligence in Flow Cytometry Diagnostics. Cytometry A 2020;97:990-3. [PMID: 32686266 DOI: 10.1002/cyto.a.24191] [Reference Citation Analysis]
28 Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 26] [Cited by in F6Publishing: 4] [Article Influence: 26.0] [Reference Citation Analysis]
29 Malik D, Mahendiratta S, Kaur H, Medhi B. Futuristic approach to cancer treatment. Gene 2021;805:145906. [PMID: 34411650 DOI: 10.1016/j.gene.2021.145906] [Reference Citation Analysis]
30 Smith SC, Gandhi JS, Moch H, Aron M, Compérat E, Paner GP, McKenney JK, Amin MB. Similarities and Differences in the 2019 ISUP and GUPS Recommendations on Prostate Cancer Grading: A Guide for Practicing Pathologists. Adv Anat Pathol 2021;28:1-7. [PMID: 33027069 DOI: 10.1097/PAP.0000000000000287] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 4.0] [Reference Citation Analysis]
31 Deng F, Zhou H, Lin Y, Heim JA, Shen L, Li Y, Zhang L. Predict multicategory causes of death in lung cancer patients using clinicopathologic factors. Comput Biol Med 2021;129:104161. [PMID: 33307409 DOI: 10.1016/j.compbiomed.2020.104161] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
32 Thomas LB, Mastorides SM, Viswanadhan NA, Jakey CE, Borkowski AA. Artificial Intelligence: Review of Current and Future Applications in Medicine. Fed Pract 2021;38:527-38. [PMID: 35136337 DOI: 10.12788/fp.0174] [Reference Citation Analysis]
33 He Y, Zhao H, Wong STC. Deep learning powers cancer diagnosis in digital pathology. Comput Med Imaging Graph 2021;88:101820. [PMID: 33453648 DOI: 10.1016/j.compmedimag.2020.101820] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
34 Sun P, He J, Chao X, Chen K, Xu Y, Huang Q, Yun J, Li M, Luo R, Kuang J, Wang H, Li H, Hui H, Xu S. A Computational Tumor-Infiltrating Lymphocyte Assessment Method Comparable with Visual Reporting Guidelines for Triple-Negative Breast Cancer. EBioMedicine 2021;70:103492. [PMID: 34280779 DOI: 10.1016/j.ebiom.2021.103492] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
35 Wang X, Wang L, Bu H, Zhang N, Yue M, Jia Z, Cai L, He J, Wang Y, Xu X, Li S, Xiao K, Yan K, Tian K, Han X, Huang J, Yao J, Liu Y. How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies. NPJ Breast Cancer 2021;7:61. [PMID: 34039982 DOI: 10.1038/s41523-021-00268-y] [Reference Citation Analysis]
36 Li J, Zhou Z, Dong J, Fu Y, Li Y, Luan Z, Peng X. Predicting breast cancer 5-year survival using machine learning: A systematic review. PLoS One 2021;16:e0250370. [PMID: 33861809 DOI: 10.1371/journal.pone.0250370] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
37 Guan X, Qin T, Qi T. Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era. Cancer Control 2022;29:107327482210773. [DOI: 10.1177/10732748221077351] [Reference Citation Analysis]
38 Bussola N, Papa B, Melaiu O, Castellano A, Fruci D, Jurman G. Quantification of the Immune Content in Neuroblastoma: Deep Learning and Topological Data Analysis in Digital Pathology. Int J Mol Sci 2021;22:8804. [PMID: 34445517 DOI: 10.3390/ijms22168804] [Reference Citation Analysis]
39 Valieris R, Amaro L, Osório CABT, Bueno AP, Rosales Mitrowsky RA, Carraro DM, Nunes DN, Dias-Neto E, Silva ITD. Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer. Cancers (Basel) 2020;12:E3687. [PMID: 33316873 DOI: 10.3390/cancers12123687] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
40 Hildebrand LA, Pierce CJ, Dennis M, Paracha M, Maoz A. Artificial Intelligence for Histology-Based Detection of Microsatellite Instability and Prediction of Response to Immunotherapy in Colorectal Cancer. Cancers (Basel). 2021;13. [PMID: 33494280 DOI: 10.3390/cancers13030391] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 5.0] [Reference Citation Analysis]
41 Zerdes I, Simonetti M, Matikas A, Harbers L, Acs B, Boyaci C, Zhang N, Salgkamis D, Agartz S, Moreno-Ruiz P, Bai Y, Rimm DL, Hartman J, Mezheyeuski A, Bergh J, Crosetto N, Foukakis T. Interplay between copy number alterations and immune profiles in the early breast cancer Scandinavian Breast Group 2004-1 randomized phase II trial: results from a feasibility study. NPJ Breast Cancer 2021;7:144. [PMID: 34799582 DOI: 10.1038/s41523-021-00352-3] [Reference Citation Analysis]
42 Ji W, Li Y, Peng H, Zhao R, Zhang X. Nature-inspired dynamic gene-loaded nanoassemblies for the treatment of brain diseases. Adv Drug Deliv Rev 2021;180:114029. [PMID: 34752841 DOI: 10.1016/j.addr.2021.114029] [Reference Citation Analysis]
43 Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Current Oncology 2022;29:1773-95. [DOI: 10.3390/curroncol29030146] [Reference Citation Analysis]
44 Zhao Y, Hu B, Wang Y, Yin X, Jiang Y, Zhu X. Identification of gastric cancer with convolutional neural networks: a systematic review. Multimed Tools Appl. [DOI: 10.1007/s11042-022-12258-8] [Reference Citation Analysis]
45 Meijering E. A bird's-eye view of deep learning in bioimage analysis. Comput Struct Biotechnol J 2020;18:2312-25. [PMID: 32994890 DOI: 10.1016/j.csbj.2020.08.003] [Cited by in Crossref: 16] [Cited by in F6Publishing: 11] [Article Influence: 8.0] [Reference Citation Analysis]
46 Parwani AV, Amin MB. Convergence of Digital Pathology and Artificial Intelligence Tools in Anatomic Pathology Practice: Current Landscape and Future Directions. Adv Anat Pathol 2020;27:221-6. [PMID: 32541593 DOI: 10.1097/PAP.0000000000000271] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
47 Aswathy MA, Jagannath M. An SVM approach towards breast cancer classification from H&E-stained histopathology images based on integrated features. Med Biol Eng Comput 2021;59:1773-83. [PMID: 34302269 DOI: 10.1007/s11517-021-02403-0] [Reference Citation Analysis]
48 Fitzgerald J, Higgins D, Mazo Vargas C, Watson W, Mooney C, Rahman A, Aspell N, Connolly A, Aura Gonzalez C, Gallagher W. Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer. J Clin Pathol 2021;74:429-34. [PMID: 34117103 DOI: 10.1136/jclinpath-2020-207351] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
49 Schuettfort VM, Pradere B, Rink M, Comperat E, Shariat SF. Pathomics in urology. Curr Opin Urol 2020;30:823-31. [PMID: 32881725 DOI: 10.1097/MOU.0000000000000813] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 6.0] [Reference Citation Analysis]
50 Zhang Y, Zhang X, Wu Q, Gu C, Wang Z. Artificial Intelligence-Aided Colonoscopy for Polyp Detection: A Systematic Review and Meta-Analysis of Randomized Clinical Trials. J Laparoendosc Adv Surg Tech A 2021. [PMID: 33524298 DOI: 10.1089/lap.2020.0777] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
51 Hofman P, Rouleau E, Sabourin JC, Denis M, Deleuze JF, Barlesi F, Laurent-Puig P. Predictive molecular pathology in non-small cell lung cancer in France: The past, the present and the perspectives. Cancer Cytopathol 2020;128:601-10. [PMID: 32885912 DOI: 10.1002/cncy.22318] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
52 Classe M, Lerousseau M, Scoazec JY, Deutsch E. Perspectives in pathomics in head and neck cancer. Curr Opin Oncol 2021;33:175-83. [PMID: 33782358 DOI: 10.1097/CCO.0000000000000731] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
53 Xie X, Wang X, Liang Y, Yang J, Wu Y, Li L, Sun X, Bing P, He B, Tian G, Shi X. Evaluating Cancer-Related Biomarkers Based on Pathological Images: A Systematic Review. Front Oncol 2021;11:763527. [PMID: 34900711 DOI: 10.3389/fonc.2021.763527] [Reference Citation Analysis]
54 Homeyer A, Lotz J, Schwen LO, Weiss N, Romberg D, Höfener H, Zerbe N, Hufnagl P. Artificial Intelligence in Pathology: From Prototype to Product. J Pathol Inform 2021;12:13. [PMID: 34012717 DOI: 10.4103/jpi.jpi_84_20] [Reference Citation Analysis]
55 Yu C, Helwig EJ. Artificial intelligence in gastric cancer: a translational narrative review. Ann Transl Med 2021;9:269. [PMID: 33708896 DOI: 10.21037/atm-20-6337] [Reference Citation Analysis]
56 Finkelman BS, Meindl A, LaBoy C, Griffin B, Narayan S, Brancamp R, Siziopikou KP, Pincus JL, Blanco LZ Jr. Correlation of manual semi-quantitative and automated quantitative Ki-67 proliferative index with OncotypeDXTM recurrence score in invasive breast carcinoma. Breast Dis 2021. [PMID: 34397396 DOI: 10.3233/BD-201011] [Reference Citation Analysis]
57 Evans T, Retzlaff CO, Geißler C, Kargl M, Plass M, Müller H, Kiehl T, Zerbe N, Holzinger A. The explainability paradox: Challenges for xAI in digital pathology. Future Generation Computer Systems 2022. [DOI: 10.1016/j.future.2022.03.009] [Reference Citation Analysis]
58 Ullah M, Akbar A, Yannarelli G. Applications of artificial intelligence in, early detection of cancer, clinical diagnosis and personalized medicine. Artif Intell Cancer 2020; 1(2): 39-44 [DOI: 10.35713/aic.v1.i2.39] [Cited by in CrossRef: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
59 Duan S, Cao H, Liu H, Miao L, Wang J, Zhou X, Wang W, Hu P, Qu L, Wu Y. Development of a machine learning-based multimode diagnosis system for lung cancer. Aging (Albany NY) 2020;12:9840-54. [PMID: 32445550 DOI: 10.18632/aging.103249] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
60 Sung YE, Kim MS, Lee YS. Proposal of a scoring system for predicting pathological risk based on a semiautomated analysis of whole slide images in oral squamous cell carcinoma. Head Neck 2021;43:1581-91. [PMID: 33533145 DOI: 10.1002/hed.26621] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
61 Kyriakopoulou K, Riti E, Piperigkou Z, Koutroumanou Sarri K, Bassiony H, Franchi M, Karamanos NK. ΕGFR/ERβ-Mediated Cell Morphology and Invasion Capacity Are Associated with Matrix Culture Substrates in Breast Cancer. Cells 2020;9:E2256. [PMID: 33050027 DOI: 10.3390/cells9102256] [Reference Citation Analysis]
62 Tătaru OS, Vartolomei MD, Rassweiler JJ, Virgil O, Lucarelli G, Porpiglia F, Amparore D, Manfredi M, Carrieri G, Falagario U, Terracciano D, de Cobelli O, Busetto GM, Del Giudice F, Ferro M. Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021;11:354. [PMID: 33672608 DOI: 10.3390/diagnostics11020354] [Cited by in Crossref: 1] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]