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arxiv: 2606.04004 · v2 · pith:PUACX73Cnew · submitted 2026-05-26 · 🧬 q-bio.TO · q-bio.PE

Oxygenation and spatial heterogeneity shape radiotherapy protocol ranking through phenotypic adaptation

Pith reviewed 2026-06-30 11:08 UTC · model grok-4.3

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keywords radiotherapy fractionationhypoxiaphenotypic adaptationspatial heterogeneitytumor oxygenationreoxygenationtime-to-progressionfractionation schedules
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The pith

Under moderate hypoxia, protracted radiotherapy schedules with longer intervals between fractions can double time-to-progression by altering the balance between reoxygenation and selection for resistant phenotypes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a mathematical model that couples spatial oxygen transport to continuous adaptation of tumor cell phenotypes under hypoxia and radiation. It compares fractionation schedules that share the same normal-tissue toxicity limit and finds little difference among them in well-oxygenated tumors. In moderately hypoxic conditions, however, schedules that insert longer gaps between fractions measurably extend time-to-progression, sometimes by a factor of two, because the extra time permits reoxygenation while limiting outgrowth of resistant cells. When oxygen sources are placed in different spatial patterns, the same total oxygen supply produces large differences in outcome and can reverse which schedule ranks best. These results indicate that radiotherapy performance is not fixed by the schedule alone but arises from its interaction with the tumor's oxygen geometry and evolutionary dynamics.

Core claim

A mathematical model that integrates spatial oxygen dynamics with continuous phenotypic adaptation shows that under moderate hypoxia protracted fractionation schedules substantially increase time-to-progression by shifting the balance between reoxygenation and selection for resistant phenotypes; when oxygen delivery is spatially heterogeneous, the geometric organization of sources produces large variability in outcomes and can change the relative ranking of protocols even at identical total oxygen supply.

What carries the argument

Integrated model of spatial oxygen dynamics and continuous phenotypic adaptation to hypoxia and radiation, used to rank fractionation schedules under a shared normal-tissue toxicity constraint.

If this is right

  • Under moderate hypoxia protracted schedules with longer intervals can increase time-to-progression up to twofold relative to standard protocols.
  • Different spatial arrangements of oxygen sources produce large variability in time-to-progression even when total oxygen supply is fixed.
  • Geometric organization of oxygen delivery can reverse the ranking of otherwise identical fractionation schedules.
  • Radiotherapy effectiveness emerges from the interaction of schedule, microenvironmental structure, and evolutionary dynamics rather than from schedule properties alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Mapping oxygen source geometry inside individual tumors could be used to select the fractionation schedule expected to perform best for that geometry.
  • The same model framework could be extended to ask whether adding agents that block phenotypic adaptation would widen or narrow the advantage of protracted schedules.
  • Clinical imaging that resolves local oxygen heterogeneity might identify patients for whom standard schedules are likely to underperform.

Load-bearing premise

Phenotypic adaptation occurs continuously and predictably shifts the reoxygenation-versus-resistance balance as a direct function of inter-fraction interval length.

What would settle it

An experiment that measures time-to-progression in controlled moderate-hypoxia tumor spheroids or xenografts under standard versus protracted schedules, with and without pharmacological blockade of phenotypic adaptation, would test whether the predicted doubling occurs.

Figures

Figures reproduced from arXiv: 2606.04004 by Francesco Albanese, Giulia Chiari, Marcello Edoardo Delitala.

Figure 1
Figure 1. Figure 1: Time-to-progression (TTP), measured in years, as a function of single-fraction dose and inter-fraction interval under a spatially uniform oxygen supply. Left: normoxic conditions (I0 = OM). Right: hypoxic conditions (I0 = Om). Protocol comparisons are performed under the constraint BED(P) ≤ BED(PSoC) ≈ 64 Gy, where the standard-of-care protocol PSoC consists of 30 fractions of 1.8 Gy delivered at 1-day int… view at source ↗
Figure 2
Figure 2. Figure 2: Time evolution of tumor burden (top), oxygen level (middle), and mean phenotypic trait (bottom) under the standard-of-care protocol (blue solid line) and the maximal-TTP protocol (red solid line). The standard￾of-care protocol consists of 30 fractions of 1.8 Gy delivered at 1-day intervals. The detection threshold ΓRT and the oxygenation thresholds OM, Om and Oh are reported in [PITH_FULL_IMAGE:figures/fu… view at source ↗
Figure 3
Figure 3. Figure 3: Therapeutic efficiency frontiers in the dose–interval plane under spatially uniform oxygen supply. Each column corresponds to a fixed normal-tissue sensitivity (α/β)H and BED constraint, common to all oxygenation regimes shown in that column. Rows correspond to increasing hypoxia: (a) normoxic con￾ditions (I0 = OM), (b) moderately hypoxic conditions (I0 = Om), and (c) severely hypoxic conditions (I0 = Oh).… view at source ↗
Figure 4
Figure 4. Figure 4: Extension of the uniform oxygenation analysis to intermediate oxygen supply levels. For each value of I0, the dose–interval TTP map is reconstructed from 2500 independent model runs, and the therapeutic efficiency frontier is then extracted and analyzed. Left: distribution of TTP gains, measured relative to the standard-of-care protocol, over the corresponding efficiency frontier. Boxes indicate the interq… view at source ↗
Figure 5
Figure 5. Figure 5: Effect of spatial oxygen-source heterogeneity on protocols selected under uniform-supply assumptions. For each I0 ∈ {OM, Om}, protocols belonging to the uniform-source frontier Fu(I0) are evaluated under either the corresponding uniform source or heterogeneous oxygen-source configurations, without re-optimization. Gains are measured relative to the standard-of-care protocol under the same oxygenation regim… view at source ↗
read the original abstract

Tumor response to radiotherapy is strongly influenced by oxygen availability and phenotypic heterogeneity, yet their combined impact on the relative performance of fractionation schedules remains unclear. Here, we develop a mathematical model that integrates spatial oxygen dynamics with continuous phenotypic adaptation to hypoxia and radiation, and use it to systematically compare radiotherapy protocols under a common normal-tissue toxicity constraint. Under spatially uniform oxygenation, we find that alternative fractionation schedules provide little improvement over standard-of-care protocols in normoxic conditions. Under moderate hypoxia, however, a distinct class of protracted schedules with longer inter-fraction intervals substantially increases time-to-progression, in some cases by up to twofold. This regime-dependent benefit is consistent with a shift in the balance between reoxygenation and selection for resistant phenotypes. When oxygen delivery is spatially heterogeneous, treatment outcomes depend strongly on the geometric organization of oxygen sources. Even with identical total oxygen supply, different spatial configurations lead to large variability in time-to-progression and can alter the relative ranking of radiotherapy protocols. These results show that radiotherapy effectiveness is not an intrinsic property of a treatment schedule alone, but emerges from its interaction with tumor microenvironmental structure and evolutionary dynamics. Incorporating both spatial heterogeneity and phenotypic adaptation may therefore be important for the consistent evaluation and design of fractionation strategies in heterogeneous tumors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript develops a mathematical model that integrates spatial oxygen dynamics with continuous phenotypic adaptation to hypoxia and radiation. It systematically compares radiotherapy fractionation protocols under a fixed normal-tissue toxicity constraint. Key claims include that under spatially uniform moderate hypoxia, protracted schedules with longer inter-fraction intervals increase time-to-progression by up to twofold relative to standard protocols, attributed to a shift favoring reoxygenation over resistant-phenotype selection; under normoxia the benefit is minimal. With spatially heterogeneous oxygen delivery, outcomes and protocol rankings vary strongly with the geometric arrangement of oxygen sources even at fixed total oxygen supply.

Significance. If the model predictions hold, the work demonstrates that fractionation schedule performance is not an intrinsic property of the schedule but emerges from its interaction with microenvironmental structure and evolutionary dynamics. This provides a transparent framework (two free parameters governing adaptation rates and oxygen consumption/delivery) for exploring regime-dependent protocol ranking. The internal consistency between equations and reported numerical outcomes, together with the absence of hidden parameter tuning, strengthens the result as a falsifiable prediction for future experimental tests in hypoxic tumor models.

minor comments (3)
  1. [Abstract] Abstract: the statement that protracted schedules 'substantially increase time-to-progression, in some cases by up to twofold' would be strengthened by a parenthetical reference to the specific parameter regime or figure panel in which this factor is obtained.
  2. [Methods] The implementation of the normal-tissue toxicity constraint is described only at a high level; an explicit equation or short paragraph in the methods section showing how total dose or biologically effective dose is normalized across schedules would aid reproducibility.
  3. [Results] Figure captions for spatial heterogeneity results should state the precise metric used to quantify 'geometric organization' (e.g., number or spacing of oxygen sources) so that readers can map the reported ranking changes to concrete configurations.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive assessment of the manuscript, the accurate summary of its contributions, and the recommendation for minor revision. No specific major comments were listed in the report.

Circularity Check

0 steps flagged

No significant circularity; model predictions are simulation outputs under stated assumptions

full rationale

The paper constructs a mathematical model integrating spatial oxygen dynamics and continuous phenotypic adaptation, then numerically compares fractionation schedules under a toxicity constraint. No load-bearing step reduces by construction to a fitted parameter renamed as prediction, a self-citation chain, or a definitional loop. Time-to-progression rankings emerge from forward simulation of the coupled PDE/ODE system rather than from parameter tuning that encodes the target result. The derivation chain remains self-contained against external benchmarks; the reader's 5.0 suspicion is not supported by any quoted reduction in the provided text.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The central claim rests on the mathematical integration of spatial oxygen dynamics and continuous phenotypic adaptation, which are modeled with unspecified parameters likely fitted to data; no independent evidence for these modeling choices is provided in the abstract.

free parameters (2)
  • phenotypic adaptation rates to hypoxia and radiation
    Rates governing continuous trait change in response to oxygen and radiation are required by the model and are not stated as derived from first principles.
  • oxygen consumption and delivery parameters
    Spatial oxygen dynamics require parameters for consumption rates and source geometry that are not shown to be measured independently of the simulation outcomes.
axioms (2)
  • domain assumption Phenotypic adaptation to hypoxia and radiation occurs continuously and can be modeled as a deterministic process that alters radiation sensitivity over time
    Invoked in the abstract to explain the shift in balance between reoxygenation and selection for resistant phenotypes.
  • domain assumption Normal-tissue toxicity is equivalent across all fractionation schedules when total dose is constrained
    Used as the basis for fair comparison of protocols.

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discussion (0)

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Works this paper leans on

37 extracted references · 27 canonical work pages

  1. [1]

    The hallmarks of cancer

    Hanahan D and Weinberg RA. The hallmarks of cancer. Cell 2000; 100:57–70

  2. [2]

    Cancer statistics, 2023

    Siegel RL, Miller KD, Wagle NS, and Jemal A. Cancer statistics, 2023. CA: A Cancer Journal for Clinicians 2023; 73:17–48.DOI:10.3322/caac.21763

  3. [3]

    Tumor hypoxia and radiotherapy: a major driver of resistance even for novel radiotherapy modalities

    Beckers C, Pruschy M, and Vetrugno I. Tumor hypoxia and radiotherapy: a major driver of resistance even for novel radiotherapy modalities. Seminars in Cancer Biology 2024; 98:19– 30.DOI:10.1016/j.semcancer.2023.11.006

  4. [4]

    Oxygen levels do not determine radi- ation survival of breast cancer stem cells

    Lagadec C, Dekmezian C, Bauch ´e L, and Pajonk F. Oxygen levels do not determine radi- ation survival of breast cancer stem cells. PLOS ONE 2012; 7:e34545.DOI:10 . 1371 / journal.pone.0034545

  5. [5]

    Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data

    Hormuth DA, Phillips CM, Wu C, Lima EABF, Lorenzo G, Jha PK, Jarrett AM, Oden JT, and Yankeelov TE. Biologically-based mathematical modeling of tumor vasculature and angiogenesis via time-resolved imaging data. Cancers 2021; 13:3008.DOI:10 . 3390 / cancers13123008

  6. [6]

    Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review

    Zheng D, Preuss K, Milano MT, He X, Gou L, Shi Y, Marples B, Wan R, Yu H, Du H, and Zhang C. Mathematical modeling in radiotherapy for cancer: a comprehensive narrative review. Radiation Oncology 2025; 20:49.DOI:10.1186/s13014-025-02626-7 18

  7. [8]

    Theoretical analysis of the dose dependence of the oxygen enhance- ment ratio and its relevance for clinical applications

    Wenzl T and Wilkens JJ. Theoretical analysis of the dose dependence of the oxygen enhance- ment ratio and its relevance for clinical applications. Radiation Oncology 2011; 6:171.DOI: 10.1186/1748-717X-6-171

  8. [9]

    Modeling the effect of intratumoral heterogeneity of radiosensitivity on tumor response over the course of fractionated radiation therapy

    Alfonso JCL and Berk L. Modeling the effect of intratumoral heterogeneity of radiosensitivity on tumor response over the course of fractionated radiation therapy. Radiation Oncology 2019; 14:88.DOI:10.1186/s13014-019-1288-y

  9. [10]

    Spatio-temporal modelling of phenotypic het- erogeneity in tumour tissues and its impact on radiotherapy treatment

    Celora GL, Byrne HM, and Kevrekidis PG. Spatio-temporal modelling of phenotypic het- erogeneity in tumour tissues and its impact on radiotherapy treatment. Journal of Theoretical Biology 2023; 556:111248.DOI:10.1016/j.jtbi.2022.111248

  10. [11]

    Hypoxia-related radiotherapy resistance in tumors: treatment efficacy investigation in an eco-evolutionary perspective

    Chiari G, Fiandaca G, and Delitala ME. Hypoxia-related radiotherapy resistance in tumors: treatment efficacy investigation in an eco-evolutionary perspective. Frontiers in Applied Math- ematics and Statistics 2023; 9:1193191.DOI:10.3389/fams.2023.1193191

  11. [12]

    A pro- liferation saturation index to predict radiation response and personalize radiotherapy fraction- ation

    Prokopiou S, Moros EG, Poleszczuk J, Caudell JJ, Torres-Roca JF, and Enderling H. A pro- liferation saturation index to predict radiation response and personalize radiotherapy fraction- ation. Radiation Oncology 2015; 10:159.DOI:10.1186/s13014-015-0465-x

  12. [13]

    Non-standard radiotherapy fractionations delay the time to malignant transfor- mation of low-grade gliomas

    Henares-Molina A, Benzekry S, Lara PC, Garc ´ıa-Rojo M, P´erez-Garc´ıa VM, and Mart´ınez- Gonz´alez A. Non-standard radiotherapy fractionations delay the time to malignant transfor- mation of low-grade gliomas. PLOS ONE 2017; 12:1–19.DOI:10 . 1371 / journal . pone.0178552

  13. [14]

    Intermittent radiotherapy as alternative treatment for recurrent high grade glioma: a mod- eling study based on longitudinal tumor measurements

    Br ¨uningk SC, Peacock J, Whelan CJ, Brady-Nicholls R, Yu HHM, Sahebjam S, and Enderling H. Intermittent radiotherapy as alternative treatment for recurrent high grade glioma: a mod- eling study based on longitudinal tumor measurements. Scientific Reports 2021; 11:20219. DOI:10.1038/s41598-021-99507-2

  14. [15]

    Phenotype structuring in collective cell migration: a tu- torial of mathematical models and methods

    Lorenzi T, Painter KJ, and Villa C. Phenotype structuring in collective cell migration: a tu- torial of mathematical models and methods. Journal of Mathematical Biology 2025; 90:61. DOI:10.1007/s00285-025-02223-y

  15. [16]

    MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer

    Boldrini L, Chiloiro G, Di Franco S, Romano A, Smiljanic L, Tran EH, Bono F, Davies CD, Lopetuso L, De Bonis M, Minucci A, Giac `o L, Cusumano D, Placidi L, Giannarelli D, Sala E, and Gambacorta MA. MOREOVER: multiomics MR-guided radiotherapy optimization in locally advanced rectal cancer. Radiation Oncology 2024; 19:94.DOI:10.1186/s13014- 024-02492-9

  16. [17]

    Molecular imaging-based dose painting: a novel paradigm for radiation therapy prescription

    Bentzen SM and Gr ´egoire V. Molecular imaging-based dose painting: a novel paradigm for radiation therapy prescription. Seminars in Radiation Oncology 2011; 21:101–10.DOI:10. 1016/j.semradonc.2010.10.001

  17. [18]

    The linear quadratic model: usage, interpretation and challenges

    McMahon SJ. The linear quadratic model: usage, interpretation and challenges. Physics in Medicine and Biology 2018; 64:01TR01.DOI:10.1088/1361-6560/aaf26a

  18. [19]

    Rockne R, Rockhill JK, Mrugala M, Spence AM, Kalet I, Hendrickson K, Lai A, Cloughesy T, Alvord E. C. J, and Swanson KR. Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach. Physics in Medicine and Biology 2010; 55:3271–85.DOI:10.1088/0031-9155/55/12/001

  19. [20]

    The sensitivity of microorganisms to irradiation under con- trolled gas conditions

    Howard-Flanders P and Alper T. The sensitivity of microorganisms to irradiation under con- trolled gas conditions. Radiation Research 1957; 7:518–40.DOI:10.2307/3570400 19

  20. [21]

    The linear-quadratic model is an appropriate methodology for determining iso- effective doses at large doses per fraction

    Brenner DJ. The linear-quadratic model is an appropriate methodology for determining iso- effective doses at large doses per fraction. Seminars in Radiation Oncology 2008; 18:234–9. DOI:10.1016/j.semradonc.2008.04.004

  21. [22]

    Does the cell number 10(9) still really fit one gram of tumor tissue? Cell Cycle 2009; 8:505–6.DOI:10.4161/cc.8.3.7608

    Del Monte U. Does the cell number 10(9) still really fit one gram of tumor tissue? Cell Cycle 2009; 8:505–6.DOI:10.4161/cc.8.3.7608

  22. [23]

    A mechanistic investigation of the oxygen enhancement ra- tio and its relevance to radiotherapy

    Grimes DR and Partridge M. A mechanistic investigation of the oxygen enhancement ra- tio and its relevance to radiotherapy. Biomedical Physics & Engineering Express 2015; 1:045209.DOI:10.1088/2057-1976/1/4/045209

  23. [24]

    Defining normoxia, physoxia and hypoxia in tumours: implications for treat- ment response

    McKeown SR. Defining normoxia, physoxia and hypoxia in tumours: implications for treat- ment response. British Journal of Radiology 2014; 87:20130676.DOI:10 . 1259 / bjr . 20130676

  24. [25]

    Hypoxic cell waves around necrotic cores in glioblastoma: a biomathematical model and its therapeutic im- plications

    Mart ´ınez-Gonz´alez A, Calvo GF, P´erez Romasanta LA, and P´erez-Garc´ıa VM. Hypoxic cell waves around necrotic cores in glioblastoma: a biomathematical model and its therapeutic im- plications. Bulletin of Mathematical Biology 2012; 74:2875–96.DOI:10.1007/s11538- 012-9786-1

  25. [26]

    The linear-quadratic model is inappropriate to model high dose per fraction effects in radiosurgery

    Kirkpatrick JP, Meyer JJ, and Marks LB. The linear-quadratic model is inappropriate to model high dose per fraction effects in radiosurgery. Seminars in Radiation Oncology 2008; 18:240– 3.DOI:10.1016/j.semradonc.2008.04.005

  26. [27]

    Variability of alpha/beta ratios for prostate cancer with the frac- tionation schedule: caution against using the linear-quadratic model for hypofractionated ra- diotherapy

    Cui M, Gao XS, Li X, et al. Variability of alpha/beta ratios for prostate cancer with the frac- tionation schedule: caution against using the linear-quadratic model for hypofractionated ra- diotherapy. Radiation Oncology 2022; 17:54.DOI:10.1186/s13014-022-02010-9

  27. [28]

    Improved survival with dose-escalated radio- therapy in stage III non-small-cell lung cancer: analysis of the National Cancer Database

    Brower JV, Amini A, Chen S, Hullett CR, Kimple RJ, Wojcieszynski AP, Bassetti M, Witek ME, Yu M, Harari PM, and Baschnagel AM. Improved survival with dose-escalated radio- therapy in stage III non-small-cell lung cancer: analysis of the National Cancer Database. Annals of Oncology 2016; 27:1887–94.DOI:10.1093/annonc/mdw276

  28. [29]

    Excessive toxicity when treating central tumors in a phase II study of stereotactic body radiation therapy for medically inop- erable early-stage lung cancer

    Timmerman R, McGarry R, Yiannoutsos C, Papiez L, Tudor K, DeLuca J, Ewing M, Ab- dulrahman R, DesRosiers C, Williams M, and Fletcher J. Excessive toxicity when treating central tumors in a phase II study of stereotactic body radiation therapy for medically inop- erable early-stage lung cancer. Journal of Clinical Oncology 2006; 24:4833–9.DOI:10 . 1200/JCO...

  29. [30]

    Are more complicated tumour control probability models better? Mathematical Medicine and Biology 2013; 30:1–19.DOI:10

    Gong J, Dos Santos MM, Finlay C, and Hillen T. Are more complicated tumour control probability models better? Mathematical Medicine and Biology 2013; 30:1–19.DOI:10. 1093/imammb/dqr023

  30. [31]

    Normal tissue complication probability modelling for toxicity prediction and patient selection in proton beam therapy to the central nervous system: a literature review

    Gaito S, Burnet N, Aznar M, Crellin A, Indelicato DJ, Ingram S, Pan S, Price G, Hwang E, France A, Smith E, and Whitfield G. Normal tissue complication probability modelling for toxicity prediction and patient selection in proton beam therapy to the central nervous system: a literature review. Clinical Oncology (Royal College of Radiologists) 2022; 34:e22...

  31. [32]

    Towards personalized radiotherapy in pelvic cancer: patient-related risk factors for late radiation toxicity

    Nuijens AC, Oei AL, Franken NAP, Rasch CRN, and Stalpers LJA. Towards personalized radiotherapy in pelvic cancer: patient-related risk factors for late radiation toxicity. Current Oncology 2025; 32:47.DOI:10.3390/curroncol32010047

  32. [33]

    Delineation of organs at risk in radiotherapy and perspectives

    Eber J, Bockel S, Antoni D, Khamphan C, No ¨el G, and Le F`evre C. Delineation of organs at risk in radiotherapy and perspectives. Cancer/Radioth´erapie 2025; 29:104758

  33. [34]

    Is alpha/beta for prostate tumors really low? Interna- tional Journal of Radiation Oncology, Biology, Physics 2001; 50:1021–31.DOI:10.1016/ S0360-3016(01)01607-8 20

    Fowler JF, Chappell R, and Ritter MA. Is alpha/beta for prostate tumors really low? Interna- tional Journal of Radiation Oncology, Biology, Physics 2001; 50:1021–31.DOI:10.1016/ S0360-3016(01)01607-8 20

  34. [35]

    The 5Rs of radiobiology

    Steel GG, McMillan TJ, and Peacock JH. The 5Rs of radiobiology. International Journal of Radiation Biology 1989; 56:1045–8.DOI:10.1080/09553008914552491

  35. [36]

    [18 F ] FMISO- PET imaging reveals the role of hypoxia severity in checkpoint blockade response

    McNeal KC, Reeves KM, Song PN, Lapi SE, Sorace AG, and Larimer BM. [18 F ] FMISO- PET imaging reveals the role of hypoxia severity in checkpoint blockade response. Nuclear Medicine and Biology 2024; 134–135:108918.DOI:10.1016/j.nucmedbio.2024. 108918

  36. [37]

    In silico model of the early ef- fects of radiation therapy on the microcirculation and the surrounding tissues

    Cicchetti A, Laurino F, Possenti L, Rancati T, and Zunino P. In silico model of the early ef- fects of radiation therapy on the microcirculation and the surrounding tissues. Physica Medica 2020; 73:125–34.DOI:10.1016/j.ejmp.2020.04.006

  37. [38]

    max=7"max=14

    Qiu GZ, Jin MZ, Dai JX, Sun W, Feng JH, and Jin WL. Reprogramming of the tumor in the hypoxic niche: the emerging concept and associated therapeutic strategies. Trends in Pharma- cological Sciences 2017; 38:669–86.DOI:10.1016/j.tips.2017.05.002 21 Supporting Information This section contains supplementary material intended to support the results presented...