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Cancers
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Application of Artificial Intelligence in Radiation Oncology
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Application of Artificial Intelligence in Radiation Oncology

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Methods and Technologies Development".

Deadline for manuscript submissions: closed (20 June 2025) | Viewed by 2151

Special Issue Editors

Dr. Xiaofeng Yang

E-Mail Website
Guest Editor
Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
Interests: image-guided intervention; multimodality medical imaging; machine learning
Dr. Lei Qiu

E-Mail Website
Guest Editor
Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
Interests: radiation therapy; segmentation; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tonghe Wang

E-Mail Website
Guest Editor
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
Interests: image segmentation; image synthesizing; diffusion models

Special Issue Information

Dear Colleagues,

Radiation oncology has long been at the forefront of technological innovation in cancer treatment. The advent of artificial intelligence (AI) marks a pivotal advancement, gaining consensus as the fourth industrial revolution. This Special Issue focuses on the integration of AI in radiation oncology, medical physics, and cancer biology, highlighting the substantial research and innovations that have emerged.

We invite submissions that explore the application of AI in various areas, including but not limited to the following:

  • Improving medical imaging and diagnostics;
  • Target and organ segmentation;
  • Treatment planning and optimization;
  • Adaptive radiation therapy;
  • Predictive modeling of treatment outcomes;
  • Enhancing patient safety and quality assurance;
  • Automating clinical workflows;
  • Personalizing cancer treatment;
  • Identifying biomarkers for treatment response.

Contributions that address ethical considerations and other related topics on AI in radiation oncology are also welcome.

Dr. Xiaofeng Yang
Dr. Lei Qiu
Dr. Tonghe Wang
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 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

  • artificial intelligence
  • radiation oncology
  • medical physics
  • cancer biology
  • medical imaging
  • segmentation
  • treatment planning
  • adaptive radiation therapy
  • predictive modeling
  • patient safety
  • clinical workflows
  • personalized treatment
  • biomarkers

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

17 pages, 3010 KiB  
Article
A Multicenter Machine Learning-Based Predictive Model of Acute Toxicity in Prostate Cancer Patients Undergoing Salvage Radiotherapy (ICAROS Study)
by Francesco Deodato, Gabriella Macchia, Patrick Duhanxhiu, Filippo Mammini, Letizia Cavallini, Maria Ntreta, Arina Alexandra Zamfir, Milly Buwenge, Francesco Cellini, Selena Ciabatti, Lorenzo Bianchi, Riccardo Schiavina, Eugenio Brunocilla, Elisa D’Angelo, Alessio Giuseppe Morganti and Savino Cilla
Cancers 2025, 17(13), 2142; https://doi.org/10.3390/cancers17132142 - 25 Jun 2025
Viewed by 287
Abstract
Background: This study aimed to develop a predictive model for acute gastrointestinal (GI) and genitourinary (GU) toxicity in prostate cancer patients treated with salvage radiotherapy (SRT) post-prostatectomy, using machine learning techniques to identify key prognostic factors. Methods: A multicenter retrospective study analyzed 454 [...] Read more.
Background: This study aimed to develop a predictive model for acute gastrointestinal (GI) and genitourinary (GU) toxicity in prostate cancer patients treated with salvage radiotherapy (SRT) post-prostatectomy, using machine learning techniques to identify key prognostic factors. Methods: A multicenter retrospective study analyzed 454 patients treated with SRT from three Italian radiotherapy centers. Acute toxicity was assessed using Radiation Therapy Oncology Group criteria. Predictors of grade ≥ 2 toxicity were identified through Least Absolute Shrinkage and Selection Operator (LASSO) regression and Classification and Regression Tree (CART) modeling. The analyzed variables included surgical technique, clinical target volume (CTV) to planning target volume (PTV) margins, extent of lymphadenectomy, radiotherapy technique, and androgen-deprivation therapy (ADT). Results: No patients experienced grade ≥ 4 toxicity, and grade 3 toxicity was below 1% for both GI and GU events. The primary determinant of acute toxicity was the surgical technique. Open prostatectomy was associated with significantly higher grade ≥ 2 GI (41.8%) and GU (35.9%) toxicity compared to laparoscopic/robotic approaches (18.9% and 12.2%, respectively). A CTV-to-PTV margin ≥ 10 mm further increased toxicity, particularly when combined with extensive lymphadenectomy. SRT technique and ADT were additional predictors in some subgroups. Conclusions: SRT demonstrated excellent tolerability. Surgical technique, CTV-to-PTV margin, and treatment parameters were key predictors of toxicity. These findings emphasize the need for personalized treatment strategies integrating surgical and radiotherapy factors to minimize toxicity and optimize outcomes in prostate cancer patients. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Radiation Oncology)
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10 pages, 4731 KiB  
Article
Quantitative Evaluation of a Fully Automated Planning Solution for Prostate-Only and Whole-Pelvic Radiotherapy
by Jessica Prunaretty, Baris Ungun, Remi Vauclin, Madalina Costea, Norbert Bus, Nikos Paragios and Pascal Fenoglietto
Cancers 2024, 16(22), 3735; https://doi.org/10.3390/cancers16223735 - 5 Nov 2024
Cited by 1 | Viewed by 1366
Abstract
Background/Objectives: To evaluate an end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments that requires minimal human input and generates a machine-deliverable plan as an output. Methods: In collaboration with TheraPanacea, a treatment planning pipeline was developed that takes as its [...] Read more.
Background/Objectives: To evaluate an end-to-end pipeline for normo-fractionated prostate-only and whole-pelvic cancer treatments that requires minimal human input and generates a machine-deliverable plan as an output. Methods: In collaboration with TheraPanacea, a treatment planning pipeline was developed that takes as its input a planning CT with organs-at-risk (OARs) and planning target volume (PTV) contours, the targeted linac machine, and the prescription dose. The primary components are (i) dose prediction by a single deep learning model for both localizations and (ii) a direct aperture VMAT plan optimization that seeks to mimic the predicted dose. The deep learning model was trained on 238 cases, and a held-out set of 86 cases was used for model validation. An end-to-end clinical evaluation study was performed on another 40 cases (20 prostate-only, 20 whole-pelvic). First, a quantitative evaluation was performed based on dose–volume histogram (DVH) points and plan parameter metrics. Then, the plan deliverability was assessed via portal dosimetry using the global gamma index. Additionally, the reference clinical manual plans were compared with the automated plans in terms of monitor unit (MU) numbers and modulation complexity scores (MCSv). Results: The automated plans provided adequate treatment plans (or minor deviations) with respect to the dose constraints, and the quality of the plans was similar to the manual plans for both localizations. Moreover, the automated plans showed successful deliverability and passed the portal dose verification. Despite higher median total MUs, no statistically significant correlation was observed between any of the gamma criteria tested and the number of MUs or MCSv. Conclusions: This study shows the feasibility of a deep learning-based fully automated treatment planning pipeline that generates high-quality plans that are competitive with manually made plans and are clinically approved in terms of dosimetry and machine deliverability. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Radiation Oncology)
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