#MathOnco Issue 69: growth dynamics, treatment dynamics, phenotypic selection, branching mutational processes, radiosensitivity
This week in
Math Oncology
June 6, 2019 ~ Issue 69
From the editor
#MathOnco friends,
Today's issue contains models of many classic Math Oncology topics: growth dynamics, treatment dynamics, phenotypic selection, branching mutational processes, and radiosensitivity. I've also linked to my most recent blog post on the concept of an "anti-fragile" tumor.
Scroll all the way down to see the new addition to the Math Oncology jobs section: postdocs modeling the evolution of genomic instability in response to DNA damaging therapy with Noemi Andor.
-Jeffrey West
Corrections & retractions: my apologies for mistakenly including an old metastasis review in last week's issue.
#MathOnco Publications
Growth dynamics in naturally progressing chronic lymphocytic leukaemia
Authors: Michaela Gruber, Ivana Bozic, Ignaty Leshchiner, Dimitri Livitz, ..., Thomas J. Kipps, Martin A. Nowak, Gad Getz & Catherine J. Wu
Modelling bistable tumour population dynamics to design effective treatment strategies
Authors: Andrei R. Akhmetzhanov, Jong Wook Kim, Ryan Sullivan, Robert A. Beckman, Pablo Tamayo, Chen-Hsiang Yeang
Modeling the effect of intratumoral heterogeneity of radiosensitivity on tumor response over the course of fractionated radiation therapy
Authors: J. C. L. Alfonso and L. Berk
Mathematical Modelling of Phenotypic Selection Within Solid Tumours
Authors: Mark A. J. Chaplain, Tommaso Lorenzi, Alexander Lorz, Chandrasekhar Venkataraman
Analysis of a tumor-model free boundary problem with a nonlinear boundary condition
Authors: Jiayue Zheng, Shangbin Cui
#MathOnco Preprints
Sensitivity Analyses for Tumor Growth Models
Authors: Ruchini Dilinika Medis
Point mutations in a growing cell population
Authors: David Cheek, Tibor Antal
The fragility of cancer treatment... or lack thereof.
Jeffrey West: The mathematical concept of "anti-fragility" strikes me as useful for 3 reasons. First, anti-fragility allows us to determine if there exists an improved schedule relative to the baseline of constant dose. Second, the sigmoidal dose-response curves ubiquitous in medicine are expected to have both convex (anti-fragile) and concave (fragile) regions. Third, perhaps most importantly: it transforms complex dose response functions into regions of binary values: benefit or harm.
#MathOnco - Book of the month
Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again
Eric Topol: "Medicine has become inhuman, to disastrous effect. The doctor-patient relationship--the heart of medicine--is broken: doctors are too distracted and overwhelmed to truly connect with their patients, and medical errors and misdiagnoses abound. In Deep Medicine, leading physician Eric Topol reveals how artificial intelligence can help."
Most clicked links of May
Jobs
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