This week in MathOnco 225
Tessellated self-assembly, multistep tumor models, circulating tumor DNA, tissue homeostasis, phenotypic switching
“This week in Mathematical Oncology” — Sept. 1, 2022
From the editor:
Today we feature articles on tessellated self-assembly, multistep tumor models, circulating tumor DNA, tissue homeostasis, phenotypic switching and more.
"I read the dictionary. I thought it was a poem about everything."
- S. Wright
Self-assembly of tessellated tissue sheets by expansion and collision
Matthew A. Heinrich, Ricard Alert, Abraham E. Wolf, Andrej Košmrlj & Daniel J. Cohen
A Multistep Tumor Growth Model of High Grade Serous Ovarian Carcinoma Identifies Hypoxia Associated Signatures
More M.H., Varankar S.S., Naik R.R., Dhake R.D., Ray P., Bankar R.M., Mali A.M., Subbalakshmi A.R., Chakraborty P., Jolly M.K., Bapat S.A.
Untangling the network effects of productivity and prominence among scientists
Weihua Li, Sam Zhang, Zhiming Zheng, Skyler J. Cranmer, Aaron Clauset
Circulating tumor DNA to guide rechallenge with panitumumab in metastatic colorectal cancer: the phase 2 CHRONOS trial
Andrea Sartore-Bianchi, Filippo Pietrantonio, Sara Lonardi, Benedetta Mussolin, …, Anna Sapino, Silvia Marsoni, Salvatore Siena, Alberto Bardelli
Thermodynamically-motivated chemo-mechanical models and multicellular simulation to provide new insight into active cell and tumour remodelling
Irish Senthilkumar, Enda Howley, Eoin McEvoy
Homeostasis limits keratinocyte evolution
Ryan O. Schenck, Eunjung Kim, Rafael R. Bravo, Jeffrey West, Simon Leedham, Darryl Shibata, Alexander R. A. Anderson
Siamese neural networks for a generalized, quantitative comparison of complex model outputs
Colin G. Cess, Stacey D. Finley
Scale-free correlations and criticality in an experimental model of brain cancer
Kevin B. Wood, Andrea Comba, Sebastien Motsch, Tomás S. Grigera, Pedro Lowenstein
Phenotype switching in a global method for agent-based models of biological tissue
Daniel Roy Bergman, Trachette L Jackson
Persistent homology derived radiomic feature predicts survival in non-small cell lung cancer patients treated with SBRT
Eashwar Somasundaram, Raoul R. Wadhwa, Adam Litzler, Rowan Barker-Clarke, …, Daniel Raymond, Kailin Yang, Michael W. Kattan, Jacob G. Scott
How does target lesion selection affect RECIST? A computer simulation study
Teresa T. Bucho, Renaud Tissier, Kevin Groot Lipman, Zuhir Bodalal, Andrea Delli Pizzi, Thi Dan Linh Nguyen-Kim, Regina Beets-Tan, Stefano Trebeschi
A holistic comparison of complex model outputs
The Mathematical Oncology Blog
Colin Cess, Stacey Finley: In our recent preprint entitled “Siamese neural networks for a generalized, quantitative comparison of complex model outputs”, we developed an approach to holistically compare model simulations without the need to specify a comparison metric. This method accounts for how model outputs are related, including relationships that would be impossible to manually calculate. We do this by using Siamese neural networks4 to project model simulations into low-dimensional space and then take the distance between projected simulations, giving us a single, continuous value that answers the question “How different are these simulations?”
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