Radiobiological Modelling in the New Era of Precision Radiation Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Therapy".

Deadline for manuscript submissions: 15 October 2024 | Viewed by 660

Special Issue Editor


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Guest Editor
Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
Interests: machine learning; radiomics; radiology; MRI; CT; PET; computer-aided diagnosis; radiobiological modelling; precision radiation oncology
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Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a comprehensive exploration of the evolving landscape of radiation oncology, with a particular focus on the integration of novel radiobiological models. Amidst the dynamic intersection of precision medicine and radiation therapy, this issue will spotlight groundbreaking advancements in image-based models, artificial intelligence applications and innovative mathematical frameworks.

Readers can anticipate a rich array of articles delving into the incorporation of cutting-edge imaging techniques to refine radiobiological predictions. Moreover, the role of artificial intelligence in optimizing treatment strategies and the introduction of new mathematical models will be thoroughly examined. These diverse models, driven by technological progress, promise to enhance our ability to tailor radiation therapies with unprecedented precision.

Through a collection of insightful contributions, this Special Issue aims to foster a deeper understanding of the synergies between radiobiological modelling, precision radiation oncology and the transformative impact of emerging technologies. The amalgamation of theoretical frameworks and practical applications will offer readers a holistic view of the exciting developments at the forefront of this rapidly evolving field.

Dr. Hamid Abdollahi
Guest Editor

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. Cancers is an international peer-reviewed open access semimonthly 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 2900 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

  • radiobiological modelling
  • precision radiation oncology
  • radiation therapy
  • cancers

Published Papers (1 paper)

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Review

41 pages, 12407 KiB  
Review
Advancements in Image-Based Models for High-Grade Gliomas Might Be Accelerated
by Guido Frosina
Cancers 2024, 16(8), 1566; https://doi.org/10.3390/cancers16081566 - 19 Apr 2024
Viewed by 269
Abstract
The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into [...] Read more.
The first half of 2022 saw the publication of several major research advances in image-based models and artificial intelligence applications to optimize treatment strategies for high-grade gliomas, the deadliest brain tumors. We review them and discuss the barriers that delay their entry into clinical practice; particularly, the small sample size and the heterogeneity of the study designs and methodologies used. We will also write about the poor and late palliation that patients suffering from high-grade glioma can count on at the end of life, as well as the current legislative instruments, with particular reference to Italy. We suggest measures to accelerate the gradual progress in image-based models and end of life care for patients with high-grade glioma. Full article
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