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For: Protopapa M, Zygogianni A, Stamatakos GS, Antypas C, Armpilia C, Uzunoglu NK, Kouloulias V. Clinical implications of in silico mathematical modeling for glioblastoma: a critical review. J Neurooncol 2018;136:1-11. [PMID: 29081039 DOI: 10.1007/s11060-017-2650-2] [Cited by in Crossref: 8] [Cited by in F6Publishing: 7] [Article Influence: 1.6] [Reference Citation Analysis]
Number Citing Articles
1 Pérez-Aliacar M, Doweidar MH, Doblaré M, Ayensa-Jiménez J. Predicting cell behaviour parameters from glioblastoma on a chip images. A deep learning approach. Comput Biol Med 2021;135:104547. [PMID: 34139437 DOI: 10.1016/j.compbiomed.2021.104547] [Reference Citation Analysis]
2 Harkos C, Svensson SF, Emblem KE, Stylianopoulos T. Inducing Biomechanical Heterogeneity in Brain Tumor Modeling by MR Elastography: Effects on Tumor Growth, Vascular Density and Delivery of Therapeutics. Cancers (Basel) 2022;14:884. [PMID: 35205632 DOI: 10.3390/cancers14040884] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Zygogianni A, Protopapa M, Kougioumtzopoulou A, Simopoulou F, Nikoloudi S, Kouloulias V. From imaging to biology of glioblastoma: new clinical oncology perspectives to the problem of local recurrence. Clin Transl Oncol 2018;20:989-1003. [PMID: 29335830 DOI: 10.1007/s12094-018-1831-6] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 1.3] [Reference Citation Analysis]
4 Fernández-romero A, Guillén-gonzález F, Suárez A. A Glioblastoma PDE-ODE model including chemotaxis and vasculature. ESAIM: M2AN 2022;56:407-31. [DOI: 10.1051/m2an/2022012] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Unkelbach J, Bortfeld T, Cardenas CE, Gregoire V, Hager W, Heijmen B, Jeraj R, Korreman SS, Ludwig R, Pouymayou B, Shusharina N, Söderberg J, Toma-Dasu I, Troost EGC, Vasquez Osorio E. The role of computational methods for automating and improving clinical target volume definition. Radiother Oncol 2020;153:15-25. [PMID: 33039428 DOI: 10.1016/j.radonc.2020.10.002] [Cited by in Crossref: 8] [Cited by in F6Publishing: 8] [Article Influence: 4.0] [Reference Citation Analysis]
6 Pérez-beteta J, Belmonte-beitia J, Pérez-garcía VM, Hubert F. Tumor width on T1-weighted MRI images of glioblastoma as a prognostic biomarker: a mathematical model. Math Model Nat Phenom 2020;15:10. [DOI: 10.1051/mmnp/2019022] [Cited by in Crossref: 6] [Cited by in F6Publishing: 1] [Article Influence: 3.0] [Reference Citation Analysis]
7 Oraiopoulou ME, Tzamali E, Tzedakis G, Liapis E, Zacharakis G, Vakis A, Papamatheakis J, Sakkalis V. Integrating in vitro experiments with in silico approaches for Glioblastoma invasion: the role of cell-to-cell adhesion heterogeneity. Sci Rep 2018;8:16200. [PMID: 30385804 DOI: 10.1038/s41598-018-34521-5] [Cited by in Crossref: 11] [Cited by in F6Publishing: 8] [Article Influence: 2.8] [Reference Citation Analysis]
8 Vollmann-Zwerenz A, Leidgens V, Feliciello G, Klein CA, Hau P. Tumor Cell Invasion in Glioblastoma. Int J Mol Sci 2020;21:E1932. [PMID: 32178267 DOI: 10.3390/ijms21061932] [Cited by in Crossref: 25] [Cited by in F6Publishing: 29] [Article Influence: 12.5] [Reference Citation Analysis]
9 Falco J, Agosti A, Vetrano IG, Bizzi A, Restelli F, Broggi M, Schiariti M, DiMeco F, Ferroli P, Ciarletta P, Acerbi F. In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case. J Clin Med 2021;10:2169. [PMID: 34067871 DOI: 10.3390/jcm10102169] [Reference Citation Analysis]
10 Salvucci M, Zakaria Z, Carberry S, Tivnan A, Seifert V, Kögel D, Murphy BM, Prehn JHM. System-based approaches as prognostic tools for glioblastoma. BMC Cancer 2019;19:1092. [PMID: 31718568 DOI: 10.1186/s12885-019-6280-2] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]