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Next-Generation Automation in Radiation Oncology: Innovations in Planning, Informatics, and Clinical Workflow

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

Deadline for manuscript submissions: 31 December 2026 | Viewed by 424

Editors

St. Jude Children's Research Hospital, Memphis, TN 38105, USA
Interests: radiation oncology; adaptive radiation therapy; motion management; pediatric cancers

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Guest Editor
St. Jude Children's Research Hospital, Memphis, TN 38105, USA
Interests: radiation oncology; radiotherapy automation; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Advances in automation are rapidly reshaping the practice of radiation oncology, driving improvements in efficiency, precision, and safety while promoting equity of cancer care. From intelligent contouring and automated treatment planning to quality assurance, data curation, and workflow orchestration, automated systems are evolving into critical components of modern radiotherapy and cancer research. As the field moves toward integrated, learning health care systems, there is an increasing need to evaluate and refine automation strategies that span the entire cancer care continuum—simulation, planning, delivery, verification, adaptation, follow-up, and outcomes assessment.

This Special Issue invites original research articles, technical developments, clinical evaluations, and comprehensive reviews focused on automation across all aspects of radiation oncology. Submissions may include, but are not limited to, the following:

  • Autosegmentation and automated contour review;
  • Knowledge-based, model-informed, or fully autonomous treatment planning;
  • Oncology workflow informatics, interoperability, and process automation;
  • Automated image guidance and intra- and inter-fractional decision support;
  • Automated workflows for offline, online, and real-time adaptive therapy ;
  • Automated quality assurance, plan checking, and error prevention;
  • Regulatory, ethical, and workforce considerations for the implementation of automation.

Our objective is to highlight emerging innovation, catalyze interdisciplinary collaboration, and showcase clinically meaningful automation that improves patient outcomes. Through this special compilation, we aim to advance the conversation about the future of radiation oncology—one where automation complements clinical expertise and elevates cancer care for all patients.

Dr. Ozgur Ates
Dr. Jared Becksfort
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • radiation oncology
  • radiotherapy automation
  • autosegmentation
  • rapid automated treatment planning
  • zero-click quality assurance
  • fast clinical workflow
  • artificial intelligence
  • machine learning
  • clinical decision support
  • radiomics
  • image-guided radiotherapy
  • adaptive radiation therapy
  • human-in-the-loop automation
  • healthcare informatics

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Published Papers (1 paper)

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Research

17 pages, 992 KB  
Article
Optimization of Automated Radiotherapy Planning for Head and Neck Cancers and Brain Tumors Using Knowledge-Based Planning Models
by Marzena Janiszewska, Tomasz Siudziński, Krzysztof Składowski and Adam J. Maciejczyk
Cancers 2026, 18(14), 2216; https://doi.org/10.3390/cancers18142216 - 9 Jul 2026
Abstract
Objectives: Our objective was to develop and evaluate a locally trained knowledge-based planning (KBP) model for head and neck (H&N), brain, and central nervous system malignancies using RapidPlan, and to determine whether standard statistical metrics such as the coefficient of determination (R2 [...] Read more.
Objectives: Our objective was to develop and evaluate a locally trained knowledge-based planning (KBP) model for head and neck (H&N), brain, and central nervous system malignancies using RapidPlan, and to determine whether standard statistical metrics such as the coefficient of determination (R2) and outlier frequency are definitive predictors of clinical utility. Methods: An institutional dataset of 594 plans was retrospectively curated into a 497-plan training set. Performance was evaluated in 370 paired plan comparisons generated with identical beam geometry. Training–validation overlap was explicitly quantified at both plan and patient levels, and a plan-level held-out sensitivity analysis was performed. Additional analyses included monitor units (MUs), subgroup assessment, clinically relevant OAR threshold achievement, and 95% confidence intervals for paired differences. Results: The validation set included 370 plans from 289 patients. At the plan level, 303 validation plans overlapped with the training model, and 67 were held-out cases; at the patient level, no fully patient-independent validation cohort was available. RapidPlan maintained target coverage while reducing OAR doses, including oral cavity Dmean (−7.62%Rx; 95% CI: −8.91 to −6.32; p < 0.001) and larynx Dmean (−7.57%Rx; 95% CI: −9.14 to −6.00; p < 0.001). The same direction of benefit was observed in the plan-level held-out subset. MU did not increase with RapidPlan and decreased from 764.2 ± 275.5 to 695.8 ± 210.3 MU (Delta = −68.4 MU; 95% CI: −89.4 to −47.4; p < 0.001). Conclusions: A high R2 was not required for clinically useful optimization objectives in this heterogeneous cohort. However, the retrospective design and patient-level overlap limit claims of full generalizability. The model should therefore be interpreted as a clinically useful standardization and decision-support tool requiring expert review rather than as a replacement for the judgment of physicists and radiation oncologists. Full article
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