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Cancers
Special Issues
New Approaches in Radiotherapy for Cancer
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New Approaches in Radiotherapy for Cancer

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 2012

Special Issue Editors

Dr. Dandan Zheng

E-Mail Website
Guest Editor
Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14626, USA
Interests: SRS/SRT/SBRT; radiomics; artificial intelligence; adaptive radiotherapy; imaging in radiotherapy; HDR
Dr. Alexander Podgorsak

E-Mail Website
Guest Editor
Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14626, USA
Interests: artificial intelligence; deep learning; image processing; radiomics; CT reconstruction
Dr. Olga Maria Dona Lemus

E-Mail Website
Guest Editor
Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14626, USA
Interests: artificial intelligence; adaptive radiotherapy; image processing; image analysis

Special Issue Information

Dear Colleagues,

In the dynamic field of radiotherapy, recent years have witnessed groundbreaking advancements that have significantly reshaped cancer treatment strategies. We have seen remarkable progress boosted by advanced imaging techniques, the integration of artificial intelligence, the development of new radiation treatment machines and online adaptive platforms, novel treatment and monitoring modalities, and a deeper understanding of radiobiology. These advances include adaptive therapy, spatially fractionated radiotherapy, FLASH, immuno-radiotherapy, proton and particle therapy, SRS for multiple brain metastases and functional diseases, radiogenomics, HDR brachytherapy, optimized beam geometry and automated treatment planning, advanced dose calculation, 3D printing, etc. Collectively, they contribute to more precise, effective and personalized radiotherapy, offering new hope and improved outcomes for patients. Therefore, this Special Issue aims to highlight and explore these innovations.

We cordially invite you to submit your cutting-edge research and review articles to this Special Issue. Your contributions will help in disseminating knowledge about these remarkable advances, fostering further research and collaboration in the field of radiotherapy. We look forward to receiving your submissions and the vibrant discussions they will undoubtedly inspire.

Dr. Dandan Zheng
Dr. Alexander Podgorsak
Dr. Olga Maria Dona Lemus
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. 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

  • adaptive radiotherapy
  • FLASH
  • artificial intelligence
  • SFRT
  • particle therapy
  • proton therapy
  • SRS
  • SBRT
  • HDR
  • 3D printing
  • immuno-radiotherapy
  • radiogenmics

Published Papers (2 papers)

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Research

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0 pages, 2752 KiB  
Article
Clinical Workflow of Cone Beam Computer Tomography-Based Daily Online Adaptive Radiotherapy with Offline Magnetic Resonance Guidance: The Modular Adaptive Radiotherapy System (MARS)
by Ji-Young Kim, Bouchra Tawk, Maximilian Knoll, Philipp Hoegen-Saßmannshausen, Jakob Liermann, Peter E. Huber, Mona Lifferth, Clemens Lang, Peter Häring, Regula Gnirs, Oliver Jäkel, Heinz-Peter Schlemmer, Jürgen Debus, Juliane Hörner-Rieber and Fabian Weykamp
Cancers 2024, 16(6), 1210; https://doi.org/10.3390/cancers16061210 - 19 Mar 2024
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Abstract
Purpose: The Ethos (Varian Medical Systems) radiotherapy device combines semi-automated anatomy detection and plan generation for cone beam computer tomography (CBCT)-based daily online adaptive radiotherapy (oART). However, CBCT offers less soft tissue contrast than magnetic resonance imaging (MRI). This work aims to present [...] Read more.
Purpose: The Ethos (Varian Medical Systems) radiotherapy device combines semi-automated anatomy detection and plan generation for cone beam computer tomography (CBCT)-based daily online adaptive radiotherapy (oART). However, CBCT offers less soft tissue contrast than magnetic resonance imaging (MRI). This work aims to present the clinical workflow of CBCT-based oART with shuttle-based offline MR guidance. Methods: From February to November 2023, 31 patients underwent radiotherapy on the Ethos (Varian, Palo Alto, CA, USA) system with machine learning (ML)-supported daily oART. Moreover, patients received weekly MRI in treatment position, which was utilized for daily plan adaptation, via a shuttle-based system. Initial and adapted treatment plans were generated using the Ethos treatment planning system. Patient clinical data, fractional session times (MRI + shuttle transport + positioning, adaptation, QA, RT delivery) and plan selection were assessed for all fractions in all patients. Results: In total, 737 oART fractions were applied and 118 MRIs for offline MR guidance were acquired. Primary sites of tumors were prostate (n = 16), lung (n = 7), cervix (n = 5), bladder (n = 1) and endometrium (n = 2). The treatment was completed in all patients. The median MRI acquisition time including shuttle transport and positioning to initiation of the Ethos adaptive session was 53.6 min (IQR 46.5–63.4). The median total treatment time without MRI was 30.7 min (IQR 24.7–39.2). Separately, median adaptation, plan QA and RT times were 24.3 min (IQR 18.6–32.2), 0.4 min (IQR 0.3–1,0) and 5.3 min (IQR 4.5–6.7), respectively. The adapted plan was chosen over the scheduled plan in 97.7% of cases. Conclusion: This study describes the first workflow to date of a CBCT-based oART combined with a shuttle-based offline approach for MR guidance. The oART duration times reported resemble the range shown by previous publications for first clinical experiences with the Ethos system. Full article
(This article belongs to the Special Issue New Approaches in Radiotherapy for Cancer)
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Review

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25 pages, 4297 KiB  
Review
Adaptive Radiotherapy: Next-Generation Radiotherapy
by Olga Maria Dona Lemus, Minsong Cao, Bin Cai, Michael Cummings and Dandan Zheng
Cancers 2024, 16(6), 1206; https://doi.org/10.3390/cancers16061206 - 19 Mar 2024
Viewed by 877
Abstract
Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes [...] Read more.
Radiotherapy, a crucial technique in cancer therapy, has traditionally relied on the premise of largely unchanging patient anatomy during the treatment course and encompassing uncertainties by target margins. This review introduces adaptive radiotherapy (ART), a notable innovation that addresses anatomy changes and optimizes the therapeutic ratio. ART utilizes advanced imaging techniques such as CT, MRI, and PET to modify the treatment plan based on observed anatomical changes and even biological changes during the course of treatment. The narrative review provides a comprehensive guide on ART for healthcare professionals and trainees in radiation oncology and anyone else interested in the topic. The incorporation of artificial intelligence in ART has played a crucial role in improving effectiveness, particularly in contour segmentation, treatment planning, and quality assurance. This has expedited the process to render online ART feasible, lowered the burden for radiation oncology practitioners, and enhanced the precision of dynamically personalized treatment. Current technical and clinical progress on ART is discussed in this review, highlighting the ongoing development of imaging technologies and AI and emphasizing their contribution to enhancing the applicability and effectiveness of ART. Full article
(This article belongs to the Special Issue New Approaches in Radiotherapy for Cancer)
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