Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours-Reference-Cited by-同舟云学术

Computational fluid dynamics with imaging of cleared tissue and of in vivo perfusion predicts drug uptake and treatment responses in tumours

Published:2018-10 Issue:10 Volume:2 Page:773-787
ISSN:2157-846X
Container-title:Nature Biomedical Engineering
language:en
Short-container-title:Nat Biomed Eng

Author:

d’Esposito Angela,Sweeney Paul W.^ORCID,Ali Morium,Saleh Magdy,Ramasawmy Rajiv,Roberts Thomas A.,Agliardi Giulia,Desjardins Adrien^ORCID,Lythgoe Mark F.,Pedley R. Barbara,Shipley Rebecca^ORCID,Walker-Samuel Simon^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Biomedical Engineering,Medicine (miscellaneous),Bioengineering,Biotechnology

Link

http://www.nature.com/articles/s41551-018-0306-y.pdf

Reference81 articles.

1. Brocato, T. et al. Understanding drug resistance in breast cancer with mathematical oncology. Curr. Breast Cancer Rep. 6, 110–120 (2014).

2. Dobosz, M., Ntziachristos, V., Scheuer, W. & Strobel, S. Multispectral fluorescence ultramicroscopy: three-dimensional visualization and automatic quantification of tumor morphology, drug penetration, and antiangiogenic treatment response. Neoplasia 16, 1–13 (2014).

3. Jahrling, N., Becker, K. & Dodt, H. U. 3D-reconstruction of blood vessels by ultramicroscopy. Organogenesis 5, 227–230 (2009).

4. Walls, J. R., Sled, J. G., Sharpe, J. & Henkelman, R. M. Resolution improvement in emission optical projection tomography. Phys. Med. Biol. 52, 2775–2790 (2007).

5. Alizadeh, A. A. et al. Toward understanding and exploiting tumor heterogeneity. Nat. Med. 21, 846–853 (2015).

Cited by 97 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Physics-informed deep generative learning for quantitative assessment of the retina;Nature Communications;2024-08-10

2. Tube2FEM: a general-purpose highly-automated pipeline for flow related processes in (embedded) tubular objects;2024-06-28

3. Unsupervised Segmentation of 3D Microvascular Photoacoustic Images Using Deep Generative Learning;Advanced Science;2024-06-23

4. A three‐dimensional, discrete‐continuum model of blood pressure in microvascular networks;International Journal for Numerical Methods in Biomedical Engineering;2024-05-21

5. Reconstructing microvascular network skeletons from 3D images: What is the ground truth?;Computers in Biology and Medicine;2024-03