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Mathematical Applications for Clinical Radiotherapy
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Mathematical Applications for Clinical Radiotherapy

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 10032

Special Issue Editors

Dr. Luis A. Pérez-Romasanta

E-Mail Website
Guest Editor
Salamanca University Hospital, University of Salamanca, Salamanca, 37008 Salamanca, Spain
Interests: radiotherapy; radiobiology; cancer
Dr. Juan Belmonte-Beitia

E-Mail Website
Guest Editor
Department of Mathematics & MOLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
Interests: radiotherapy; radiobiology, cancer; mathematics
Dr. Gabriel F. Calvo

E-Mail Website
Guest Editor
Department of Mathematics & MOLAB-Mathematical Oncology Laboratory, University of Castilla-La Mancha, 13071 Ciudad Real, Spain
Interests: radiotherapy; radiobiology, cancer; mathematics

Special Issue Information

Dear Colleagues,

Cancer is a complex disease. An adequate description of cancer can only be obtained by integration of multiple interdependent biological mechanisms in tumor cells and in the tumor microenvironment, including the immune system. Computational tools can facilitate the analysis of cancer as a complex and dynamic biological system contributing to create a global description of the diverse biological forces driving tumorigenesis, metastasis, treatment effects and, finally, cure probability.  Mathematical intelligence of cancer may be a valuable tool to define new classifications, predictions, and research strategies not only in laboratories, but also in the clinic.

Radiation oncology has a distinguished history as a forerunner of personalized treatment modality in clinical oncology. Besides treatment planning, mathematical modelling has addressed the biological effects of ionizing radiation, not only its cell killing capacity, but also its potential role in the emergence of tumor resistance. In recent years the armamentarium of radiation-based therapies, alone or combined with other treatments, has witnessed a significant development which demands the intensive use of novel quantitative approaches to the existing roaster.

This Special Issue of the Journal of Personalized Medicine is deployed to highlight the current state of the mathematical applications for radiation oncology and showcase some of the latest findings in the field of radiotherapy effects modelling.

Dr. Luis A. Pérez-Romasanta
Dr. Juan Belmonte-Beitia
Dr. Gabriel F. Calvo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational biology
  • mathematical modelling
  • radiobiology
  • radiotherapy
  • cancer
  • prognostic factors

Published Papers (4 papers)

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Research

12 pages, 2203 KiB  
Article
Dynamics-Adapted Radiotherapy Dose (DARD) for Head and Neck Cancer Radiotherapy Dose Personalization
by Mohammad U. Zahid, Abdallah S. R. Mohamed, Jimmy J. Caudell, Louis B. Harrison, Clifton D. Fuller, Eduardo G. Moros and Heiko Enderling
J. Pers. Med. 2021, 11(11), 1124; https://doi.org/10.3390/jpm11111124 - 01 Nov 2021
Cited by 13 | Viewed by 2556
Abstract
Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66–70 Gy, this is done without [...] Read more.
Standard of care radiotherapy (RT) doses have been developed as a one-size-fits all approach designed to maximize tumor control rates across a population. Although this has led to high control rates for head and neck cancer with 66–70 Gy, this is done without considering patient heterogeneity. We present a framework to estimate a personalized RT dose for individual patients, based on pre- and early on-treatment tumor volume dynamics—a dynamics-adapted radiotherapy dose (DDARD). We also present the results of an in silico trial of this dose personalization using retrospective data from a combined cohort of n = 39 head and neck cancer patients from the Moffitt and MD Anderson Cancer Centers that received 66–70 Gy RT in 2–2.12 Gy weekday fractions. This trial was repeated constraining DDARD between (54, 82) Gy to test more moderate dose adjustment. DDARD was estimated to range from 8 to 186 Gy, and our in silico trial estimated that 77% of patients treated with standard of care were overdosed by an average dose of 39 Gy, and 23% underdosed by an average dose of 32 Gy. The in silico trial with constrained dose adjustment estimated that locoregional control could be improved by >10%. We demonstrated the feasibility of using early treatment tumor volume dynamics to inform dose personalization and stratification for dose escalation and de-escalation. These results demonstrate the potential to both de-escalate most patients, while still improving population-level control rates. Full article
(This article belongs to the Special Issue Mathematical Applications for Clinical Radiotherapy)
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20 pages, 1553 KiB  
Article
Optimal Combinations of Chemotherapy and Radiotherapy in Low-Grade Gliomas: A Mathematical Approach
by Luis E. Ayala-Hernández, Armando Gallegos, Philippe Schucht, Michael Murek, Luis Pérez-Romasanta, Juan Belmonte-Beitia and Víctor M. Pérez-García
J. Pers. Med. 2021, 11(10), 1036; https://doi.org/10.3390/jpm11101036 - 16 Oct 2021
Cited by 10 | Viewed by 2927
Abstract
Low-grade gliomas (LGGs) are brain tumors characterized by their slow growth and infiltrative nature. Treatment options for these tumors are surgery, radiation therapy and chemotherapy. The optimal use of radiation therapy and chemotherapy is still under study. In this paper, we construct a [...] Read more.
Low-grade gliomas (LGGs) are brain tumors characterized by their slow growth and infiltrative nature. Treatment options for these tumors are surgery, radiation therapy and chemotherapy. The optimal use of radiation therapy and chemotherapy is still under study. In this paper, we construct a mathematical model of LGG response to combinations of chemotherapy, specifically to the alkylating agent temozolomide and radiation therapy. Patient-specific parameters were obtained from longitudinal imaging data of the response of real LGG patients. Computer simulations showed that concurrent cycles of radiation therapy and temozolomide could provide the best therapeutic efficacy in-silico for the patients included in the study. The patient cohort was extended computationally to a set of 3000 virtual patients. This virtual cohort was subject to an in-silico trial in which matching the doses of radiotherapy to those of temozolomide in the first five days of each cycle improved overall survival over concomitant radio-chemotherapy according to RTOG 0424. Thus, the proposed treatment schedule could be investigated in a clinical setting to improve combination treatments in LGGs with substantial survival benefits. Full article
(This article belongs to the Special Issue Mathematical Applications for Clinical Radiotherapy)
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18 pages, 981 KiB  
Article
The Effect of Radiotherapy on Diffuse Low-Grade Gliomas Evolution: Confronting Theory with Clinical Data
by Léo Adenis, Stéphane Plaszczynski, Basile Grammaticos, Johan Pallud and Mathilde Badoual
J. Pers. Med. 2021, 11(8), 818; https://doi.org/10.3390/jpm11080818 - 21 Aug 2021
Cited by 5 | Viewed by 1708
Abstract
Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated [...] Read more.
Diffuse low-grade gliomas are slowly growing tumors that always recur after treatment. In this paper, we revisit the modeling of the evolution of the tumor radius before and after the radiotherapy process and propose a novel model that is simple yet biologically motivated and that remedies some shortcomings of previously proposed ones. We confront this with clinical data consisting of time series of tumor radii from 43 patient records by using a stochastic optimization technique and obtain very good fits in all cases. Since our model describes the evolution of a tumor from the very first glioma cell, it gives access to the possible age of the tumor. Using the technique of profile likelihood to extract all of the information from the data, we build confidence intervals for the tumor birth age and confirm the fact that low-grade gliomas seem to appear in the late teenage years. Moreover, an approximate analytical expression of the temporal evolution of the tumor radius allows us to explain the correlations observed in the data. Full article
(This article belongs to the Special Issue Mathematical Applications for Clinical Radiotherapy)
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11 pages, 308 KiB  
Article
Non-Homogeneous Tumor Growth and Its Implications for Radiotherapy: A Phenomenological Approach
by Paolo Castorina, Luigi Castorina and Gianluca Ferini
J. Pers. Med. 2021, 11(6), 527; https://doi.org/10.3390/jpm11060527 - 09 Jun 2021
Cited by 10 | Viewed by 2001
Abstract
Tumor regrowth and heterogeneity are important clinical parameters during radiotherapy, and the probability of treatment benefit critically depends on the tumor progression pattern in the interval between the fractional irradiation treatments. We propose an analytic, easy-to-use method to take into account clonal subpopulations [...] Read more.
Tumor regrowth and heterogeneity are important clinical parameters during radiotherapy, and the probability of treatment benefit critically depends on the tumor progression pattern in the interval between the fractional irradiation treatments. We propose an analytic, easy-to-use method to take into account clonal subpopulations with different specific growth rates and radiation resistances. The different strain regrowth effects, as described by Gompertz law, require a dose-boost to reproduce the survival probability of the corresponding homogeneous system and for uniform irradiation. However, the estimate of the survival fraction for a tumor with a hypoxic subpopulation is more reliable when there is a slow specific regrowth rate and when the dependence on the oxygen enhancement ratio of radiotherapy is consistently taken into account. The approach is discussed for non-linear two-population dynamics for breast cancer and can be easily generalized to a larger number of components and different tumor phenotypes. Full article
(This article belongs to the Special Issue Mathematical Applications for Clinical Radiotherapy)
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