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Cancers
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Radiobiological Modelling in the New Era of Precision Radiation Oncology
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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 1573

Special Issue Editor

Dr. Hamid Abdollahi

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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 (2 papers)

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Research

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18 pages, 917 KiB  
Article
Segmentation-Free Outcome Prediction from Head and Neck Cancer PET/CT Images: Deep Learning-Based Feature Extraction from Multi-Angle Maximum Intensity Projections (MA-MIPs)
by Amirhosein Toosi, Isaac Shiri, Habib Zaidi and Arman Rahmim
Cancers 2024, 16(14), 2538; https://doi.org/10.3390/cancers16142538 - 14 Jul 2024
Viewed by 251
Abstract
We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our [...] Read more.
We introduce an innovative, simple, effective segmentation-free approach for survival analysis of head and neck cancer (HNC) patients from PET/CT images. By harnessing deep learning-based feature extraction techniques and multi-angle maximum intensity projections (MA-MIPs) applied to Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) images, our proposed method eliminates the need for manual segmentations of regions-of-interest (ROIs) such as primary tumors and involved lymph nodes. Instead, a state-of-the-art object detection model is trained utilizing the CT images to perform automatic cropping of the head and neck anatomical area, instead of only the lesions or involved lymph nodes on the PET volumes. A pre-trained deep convolutional neural network backbone is then utilized to extract deep features from MA-MIPs obtained from 72 multi-angel axial rotations of the cropped PET volumes. These deep features extracted from multiple projection views of the PET volumes are then aggregated and fused, and employed to perform recurrence-free survival analysis on a cohort of 489 HNC patients. The proposed approach outperforms the best performing method on the target dataset for the task of recurrence-free survival analysis. By circumventing the manual delineation of the malignancies on the FDG PET-CT images, our approach eliminates the dependency on subjective interpretations and highly enhances the reproducibility of the proposed survival analysis method. The code for this work is publicly released. Full article
(This article belongs to the Special Issue Radiobiological Modelling in the New Era of Precision Radiation Oncology)

Review

Jump to: Research

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 821
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
(This article belongs to the Special Issue Radiobiological Modelling in the New Era of Precision Radiation Oncology)
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