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Cancers
Special Issues
Image-Guided Adaptive Radiation Therapy (IGART): Advancing Precision Oncology
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Image-Guided Adaptive Radiation Therapy (IGART): Advancing Precision Oncology

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 2025 | Viewed by 966

Special Issue Editors

Prof. Dr. An Liu

E-Mail Website
Guest Editor
City of Hope National Medical Center, Duarte, CA, USA
Interests: radiation oncology; imaging; physics; tumors affecting the brain, head & neck, thoracic, abdomen and pelvic regions
Dr. Bin Cai

E-Mail Website
Guest Editor
Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
Interests: technical and image-guided radiotherapy; radiology
Dr. Murat Surucu

E-Mail Website
Guest Editor
Department of Radiation Oncology—Radiation Physics, Stanford University School of Medicine, Stanford, CA, USA
Interests: radiology; oncology

Special Issue Information

Dear Colleagues,

As the field of radiation therapy continues to evolve, IGART represents one of the most transformative advancements, offering unprecedented precision in tumor targeting while sparing healthy tissues.

This Special Issue aims to highlight recent innovations, clinical applications, and future directions of IGART. We are particularly interested in

  • Advanced imaging techniques (CT, MR, PET/CT) and their integration with adaptive radiotherapy.
  • Clinical outcomes of IGART in various cancer types.
  • Computational models and algorithms for real-time adaptation.
  • Overcoming challenges in implementing IGART in clinical practice.
  • Cost-effectiveness and patient-centered approaches in adaptive radiation therapy.

Prof. Dr. An Liu
Dr. Bin Cai
Dr. Murat Surucu
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

  • advanced imaging techniques
  • adaptive radiotherapy
  • computational models

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Published Papers (2 papers)

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Research

15 pages, 2884 KiB  
Article
Strategies for Offline Adaptive Biology-Guided Radiotherapy (BgRT) on a PET-Linac Platform
by Bin Cai, Thomas I. Banks, Chenyang Shen, Rameshwar Prasad, Girish Bal, Mu-Han Lin, Andrew Godley, Arnold Pompos, Aurelie Garant, Kenneth Westover, Tu Dan, Steve Jiang, David Sher, Orhan K. Oz, Robert Timmerman and Shahed N. Badiyan
Cancers 2025, 17(15), 2470; https://doi.org/10.3390/cancers17152470 - 25 Jul 2025
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Abstract
Background/Objectives: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomical and biological changes. Methods: We propose a decision tree offline adaptation framework based [...] Read more.
Background/Objectives: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomical and biological changes. Methods: We propose a decision tree offline adaptation framework based on real-time assessments of Activity Concentration (AC), Normalized Target Signal (NTS), and bounded dose-volume histogram (bDVH%) metrics. Three offline strategies were developed: (1) preemptive adaptation for minor changes, (2) partial re-simulation for moderate changes, and (3) full re-simulation for major anatomical or metabolic alterations. Two clinical cases demonstrating strategies 1 and 2 are presented. Results: The preemptive adaptation strategy was applied in a case with early tumor shrinkage, maintaining delivery parameters within acceptable limits while updating contours and dose distribution. In the partial re-Simulation case, significant changes in PET signal necessitated a same-day PET functional modeling session and plan re-optimization, effectively restoring safe deliverability. Both cases showed reduced target volumes and improved OAR sparing without additional patient visits or tracer injections. Conclusions: Offline adaptive workflows for BgRT provide practical solutions to address inter-fractional changes in tumor structure and function. These strategies can help maintain the safety and accuracy of BgRT delivery and support clinical adoption of PET-guided radiotherapy, paving the way for future online adaptive capabilities. Full article
(This article belongs to the Special Issue Image-Guided Adaptive Radiation Therapy (IGART): Advancing Precision Oncology)
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14 pages, 1509 KiB  
Article
A Multi-Modal Deep Learning Approach for Predicting Eligibility for Adaptive Radiation Therapy in Nasopharyngeal Carcinoma Patients
by Zhichun Li, Zihan Li, Sai Kit Lam, Xiang Wang, Peilin Wang, Liming Song, Francis Kar-Ho Lee, Celia Wai-Yi Yip, Jing Cai and Tian Li
Cancers 2025, 17(14), 2350; https://doi.org/10.3390/cancers17142350 - 15 Jul 2025
Viewed by 336
Abstract
Background: Adaptive radiation therapy (ART) can improve prognosis for nasopharyngeal carcinoma (NPC) patients. However, the inter-individual variability in anatomical changes, along with the resulting extension of treatment duration and increased workload for the radiologists, makes the selection of eligible patients a persistent challenge [...] Read more.
Background: Adaptive radiation therapy (ART) can improve prognosis for nasopharyngeal carcinoma (NPC) patients. However, the inter-individual variability in anatomical changes, along with the resulting extension of treatment duration and increased workload for the radiologists, makes the selection of eligible patients a persistent challenge in clinical practice. The purpose of this study was to predict eligible ART candidates prior to radiation therapy (RT) for NPC patients using a classification neural network. By leveraging the fusion of medical imaging and clinical data, this method aimed to save time and resources in clinical workflows and improve treatment efficiency. Methods: We collected retrospective data from 305 NPC patients who received RT at Hong Kong Queen Elizabeth Hospital. Each patient sample included pre-treatment computed tomographic (CT) images, T1-weighted magnetic resonance imaging (MRI) data, and T2-weighted MRI images, along with clinical data. We developed and trained a novel multi-modal classification neural network that combines ResNet-50, cross-attention, multi-scale features, and clinical data for multi-modal fusion. The patients were categorized into two labels based on their re-plan status: patients who received ART during RT treatment, as determined by the radiation oncologist, and those who did not. Results: The experimental results demonstrated that the proposed multi-modal deep prediction model outperformed other commonly used deep learning networks, achieving an area under the curve (AUC) of 0.9070. These results indicated the ability of the model to accurately classify and predict ART eligibility for NPC patients. Conclusions: The proposed method showed good performance in predicting ART eligibility among NPC patients, highlighting its potential to enhance clinical decision-making, optimize treatment efficiency, and support more personalized cancer care. Full article
(This article belongs to the Special Issue Image-Guided Adaptive Radiation Therapy (IGART): Advancing Precision Oncology)
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