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Cited by in F6Publishing
For: Weis CA, Kather JN, Melchers S, Al-Ahmdi H, Pollheimer MJ, Langner C, Gaiser T. Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome. Diagn Pathol. 2018;13:64. [PMID: 30153844 DOI: 10.1186/s13000-018-0739-3] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 3.8] [Reference Citation Analysis]
Number Citing Articles
1 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]
2 Mäkitie AA, Almangush A, Rodrigo JP, Ferlito A, Leivo I. Hallmarks of cancer: Tumor budding as a sign of invasion and metastasis in head and neck cancer. Head Neck 2019;41:3712-8. [PMID: 31328847 DOI: 10.1002/hed.25872] [Cited by in Crossref: 15] [Cited by in F6Publishing: 13] [Article Influence: 5.0] [Reference Citation Analysis]
3 Studer L, Blank A, Bokhorst JM, Nagtegaal ID, Zlobec I, Lugli A, Fischer A, Dawson H. Taking tumour budding to the next frontier - a post International Tumour Budding Consensus Conference (ITBCC) 2016 review. Histopathology 2021;78:476-84. [PMID: 33001500 DOI: 10.1111/his.14267] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
4 Pai RK, Hartman D, Schaeffer DF, Rosty C, Shivji S, Kirsch R, Pai RK. Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters. Histopathology 2021;79:391-405. [PMID: 33590485 DOI: 10.1111/his.14353] [Cited by in Crossref: 1] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
5 Abd Raboh NM, Mady OM, Hakim SA. Evaluation of the Potential Prognostic Value of Tumor Budding in Laryngeal Carcinoma by Conventional and Immunohistochemical Staining. Anal Cell Pathol (Amst) 2020;2020:9183671. [PMID: 33274177 DOI: 10.1155/2020/9183671] [Reference Citation Analysis]
6 Yoshida H, Kiyuna T. Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology. World J Gastroenterol 2021; 27(21): 2818-2833 [PMID: 34135556 DOI: 10.3748/wjg.v27.i21.2818] [Reference Citation Analysis]
7 Brieu N, Gavriel CG, Nearchou IP, Harrison DJ, Schmidt G, Caie PD. Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis. Sci Rep 2019;9:5174. [PMID: 30914794 DOI: 10.1038/s41598-019-41595-2] [Cited by in Crossref: 16] [Cited by in F6Publishing: 14] [Article Influence: 5.3] [Reference Citation Analysis]
8 Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40:154-166. [PMID: 32277744 DOI: 10.1002/cac2.12012] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 8.5] [Reference Citation Analysis]
9 Bokhorst JM, Blank A, Lugli A, Zlobec I, Dawson H, Vieth M, Rijstenberg LL, Brockmoeller S, Urbanowicz M, Flejou JF, Kirsch R, Ciompi F, van der Laak JAWM, Nagtegaal ID. Assessment of individual tumor buds using keratin immunohistochemistry: moderate interobserver agreement suggests a role for machine learning. Mod Pathol 2020;33:825-33. [PMID: 31844269 DOI: 10.1038/s41379-019-0434-2] [Cited by in Crossref: 12] [Cited by in F6Publishing: 13] [Article Influence: 4.0] [Reference Citation Analysis]
10 Cianci P, Restini E. Artificial intelligence in colorectal cancer management. Artif Intell Cancer 2021; 2(6): 79-89 [DOI: 10.35713/aic.v2.i6.79] [Reference Citation Analysis]
11 Schiele S, Arndt TT, Martin B, Miller S, Bauer S, Banner BM, Brendel EM, Schenkirsch G, Anthuber M, Huss R, Märkl B, Müller G. Deep Learning Prediction of Metastasis in Locally Advanced Colon Cancer Using Binary Histologic Tumor Images. Cancers (Basel) 2021;13:2074. [PMID: 33922988 DOI: 10.3390/cancers13092074] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
12 Rasmusson A, Zilenaite D, Nestarenkaite A, Augulis R, Laurinaviciene A, Ostapenko V, Poskus T, Laurinavicius A. Immunogradient Indicators for Antitumor Response Assessment by Automated Tumor-Stroma Interface Zone Detection. Am J Pathol 2020;190:1309-22. [PMID: 32194048 DOI: 10.1016/j.ajpath.2020.01.018] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
13 Alpsoy A, Yavuz A, Elpek GO. Artificial intelligence in pathological evaluation of gastrointestinal cancers. Artif Intell Gastroenterol 2021; 2(6): 141-156 [DOI: 10.35712/aig.v2.i6.141] [Reference Citation Analysis]
14 Lugli A, Zlobec I, Berger MD, Kirsch R, Nagtegaal ID. Tumour budding in solid cancers. Nat Rev Clin Oncol 2021;18:101-15. [PMID: 32901132 DOI: 10.1038/s41571-020-0422-y] [Cited by in Crossref: 18] [Cited by in F6Publishing: 17] [Article Influence: 9.0] [Reference Citation Analysis]
15 Nestarenkaite A, Fadhil W, Rasmusson A, Susanti S, Hadjimichael E, Laurinaviciene A, Ilyas M, Laurinavicius A. Immuno-Interface Score to Predict Outcome in Colorectal Cancer Independent of Microsatellite Instability Status. Cancers (Basel) 2020;12:E2902. [PMID: 33050344 DOI: 10.3390/cancers12102902] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
16 Pacal I, Karaboga D, Basturk A, Akay B, Nalbantoglu U. A comprehensive review of deep learning in colon cancer. Computers in Biology and Medicine 2020;126:104003. [DOI: 10.1016/j.compbiomed.2020.104003] [Cited by in Crossref: 13] [Cited by in F6Publishing: 7] [Article Influence: 6.5] [Reference Citation Analysis]
17 Laurinavicius A, Rasmusson A, Plancoulaine B, Shribak M, Levenson R. Machine-Learning-Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance. Am J Pathol 2021:S0002-9440(21)00165-6. [PMID: 33895120 DOI: 10.1016/j.ajpath.2021.04.008] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
18 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]