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Cited by in F6Publishing
For: Hormuth DA 2nd, Weis JA, Barnes SL, Miga MI, Quaranta V, Yankeelov TE. Biophysical Modeling of In Vivo Glioma Response After Whole-Brain Radiation Therapy in a Murine Model of Brain Cancer. Int J Radiat Oncol Biol Phys 2018;100:1270-9. [PMID: 29398129 DOI: 10.1016/j.ijrobp.2017.12.004] [Cited by in Crossref: 11] [Cited by in F6Publishing: 13] [Article Influence: 2.2] [Reference Citation Analysis]
Number Citing Articles
1 Liu J, Hormuth DA, Davis T, Yang J, McKenna MT, Jarrett AM, Enderling H, Brock A, Yankeelov TE. A time-resolved experimental-mathematical model for predicting the response of glioma cells to single-dose radiation therapy. Integr Biol (Camb) 2021;13:167-83. [PMID: 34060613 DOI: 10.1093/intbio/zyab010] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
2 Jarrett AM, Kazerouni AS, Wu C, Virostko J, Sorace AG, DiCarlo JC, Hormuth DA 2nd, Ekrut DA, Patt D, Goodgame B, Avery S, Yankeelov TE. Quantitative magnetic resonance imaging and tumor forecasting of breast cancer patients in the community setting. Nat Protoc 2021;16:5309-38. [PMID: 34552262 DOI: 10.1038/s41596-021-00617-y] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Hormuth DA 2nd, Jarrett AM, Lima EABF, McKenna MT, Fuentes DT, Yankeelov TE. Mechanism-Based Modeling of Tumor Growth and Treatment Response Constrained by Multiparametric Imaging Data. JCO Clin Cancer Inform 2019;3:1-10. [PMID: 30807209 DOI: 10.1200/CCI.18.00055] [Cited by in Crossref: 9] [Cited by in F6Publishing: 10] [Article Influence: 4.5] [Reference Citation Analysis]
4 Hormuth DA 2nd, Al Feghali KA, Elliott AM, Yankeelov TE, Chung C. Image-based personalization of computational models for predicting response of high-grade glioma to chemoradiation. Sci Rep 2021;11:8520. [PMID: 33875739 DOI: 10.1038/s41598-021-87887-4] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
5 Jarrett AM, Hormuth DA, Adhikarla V, Sahoo P, Abler D, Tumyan L, Schmolze D, Mortimer J, Rockne RC, Yankeelov TE. Towards integration of 64Cu-DOTA-trastuzumab PET-CT and MRI with mathematical modeling to predict response to neoadjuvant therapy in HER2 + breast cancer. Sci Rep 2020;10:20518. [PMID: 33239688 DOI: 10.1038/s41598-020-77397-0] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
6 Jarrett AM, Lima EABF, Hormuth DA 2nd, McKenna MT, Feng X, Ekrut DA, Resende ACM, Brock A, Yankeelov TE. Mathematical models of tumor cell proliferation: A review of the literature. Expert Rev Anticancer Ther 2018;18:1271-86. [PMID: 30252552 DOI: 10.1080/14737140.2018.1527689] [Cited by in Crossref: 28] [Cited by in F6Publishing: 21] [Article Influence: 7.0] [Reference Citation Analysis]
7 Lorenzo G, Pérez-García VM, Mariño A, Pérez-Romasanta LA, Reali A, Gomez H. Mechanistic modelling of prostate-specific antigen dynamics shows potential for personalized prediction of radiation therapy outcome. J R Soc Interface 2019;16:20190195. [PMID: 31409240 DOI: 10.1098/rsif.2019.0195] [Cited by in Crossref: 10] [Cited by in F6Publishing: 2] [Article Influence: 3.3] [Reference Citation Analysis]
8 Haopeng P, Xuefei D, Zengai C, Zhenwei Y, Chien-Shan C, Zhiqiang M. High-Resolution Diffusion-Weighted Imaging of C6 Glioma on a 7T BioSpec MRI Scanner: Correlation of Tumor Cellularity and Nuclear-to-Cytoplasmic Ratio with Apparent Diffusion Coefficient. Acad Radiol 2022;29 Suppl 3:S80-7. [PMID: 34148856 DOI: 10.1016/j.acra.2021.02.009] [Reference Citation Analysis]
9 Hormuth DA 2nd, Jarrett AM, Yankeelov TE. Forecasting tumor and vasculature response dynamics to radiation therapy via image based mathematical modeling. Radiat Oncol 2020;15:4. [PMID: 31898514 DOI: 10.1186/s13014-019-1446-2] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 4.5] [Reference Citation Analysis]
10 Hormuth DA 2nd, Farhat M, Christenson C, Curl B, Chad Quarles C, Chung C, Yankeelov TE. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy. Adv Drug Deliv Rev 2022;187:114367. [PMID: 35654212 DOI: 10.1016/j.addr.2022.114367] [Reference Citation Analysis]
11 Liu J, Hormuth DA, Yang J, Yankeelov TE. A Multi-Compartment Model of Glioma Response to Fractionated Radiation Therapy Parameterized via Time-Resolved Microscopy Data. Front Oncol 2022;12:811415. [DOI: 10.3389/fonc.2022.811415] [Reference Citation Analysis]
12 Jarrett AM, Hormuth DA, Barnes SL, Feng X, Huang W, Yankeelov TE. Incorporating drug delivery into an imaging-driven, mechanics-coupled reaction diffusion model for predicting the response of breast cancer to neoadjuvant chemotherapy: theory and preliminary clinical results. Phys Med Biol 2018;63:105015. [PMID: 29697054 DOI: 10.1088/1361-6560/aac040] [Cited by in Crossref: 18] [Cited by in F6Publishing: 16] [Article Influence: 4.5] [Reference Citation Analysis]
13 Elazab A, Anter AM, Bai H, Hu Q, Hussain Z, Ni D, Wang T, Lei B. An optimized generic cerebral tumor growth modeling framework by coupling biomechanical and diffusive models with treatment effects. Applied Soft Computing 2019;80:617-27. [DOI: 10.1016/j.asoc.2019.04.034] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
14 Hormuth DA 2nd, Jarrett AM, Feng X, Yankeelov TE. Calibrating a Predictive Model of Tumor Growth and Angiogenesis with Quantitative MRI. Ann Biomed Eng 2019;47:1539-51. [PMID: 30963385 DOI: 10.1007/s10439-019-02262-9] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 6.3] [Reference Citation Analysis]
15 Ji H, Lafata K, Mowery Y, Brizel D, Bertozzi AL, Yin F, Wang C. Post-Radiotherapy PET Image Outcome Prediction by Deep Learning Under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application. Front Oncol 2022;12:895544. [DOI: 10.3389/fonc.2022.895544] [Reference Citation Analysis]
16 Jarrett AM, Hormuth DA 2nd, Wu C, Kazerouni AS, Ekrut DA, Virostko J, Sorace AG, DiCarlo JC, Kowalski J, Patt D, Goodgame B, Avery S, Yankeelov TE. Evaluating patient-specific neoadjuvant regimens for breast cancer via a mathematical model constrained by quantitative magnetic resonance imaging data. Neoplasia 2020;22:820-30. [PMID: 33197744 DOI: 10.1016/j.neo.2020.10.011] [Cited by in Crossref: 6] [Cited by in F6Publishing: 5] [Article Influence: 3.0] [Reference Citation Analysis]
17 Hormuth DA 2nd, Sorace AG, Virostko J, Abramson RG, Bhujwalla ZM, Enriquez-Navas P, Gillies R, Hazle JD, Mason RP, Quarles CC, Weis JA, Whisenant JG, Xu J, Yankeelov TE. Translating preclinical MRI methods to clinical oncology. J Magn Reson Imaging 2019;50:1377-92. [PMID: 30925001 DOI: 10.1002/jmri.26731] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 4.3] [Reference Citation Analysis]