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

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5887

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Special Issue Editors

Prof. Dr. An Liu

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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

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Guest Editor

Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, USA
Interests: technical and image-guided radiotherapy; radiology

Dr. Murat Surucu

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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 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-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 (4 papers)

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Research

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14 pages, 2895 KB

Open AccessArticle

Interpretable and Performant Multimodal Nasopharyngeal Carcinoma GTV Segmentation with Clinical Priors Guided 3D-Gaussian-Prompted Diffusion Model (3DGS-PDM)

by Jiarui Zhu, Zongrui Ma, Ge Ren and Jing Cai

Cancers 2025, 17(22), 3660; https://doi.org/10.3390/cancers17223660 - 14 Nov 2025

Viewed by 907

Abstract

Background: Gross tumor volume (GTV) segmentation of Nasopharyngeal Carcinoma (NPC) crucially determines the precision of image-guided radiation therapy (IGRT) for NPC. Compared to other cancers, the clinical delineation of NPC is especially challenging due to its capricious infiltration of the adjacent rich tissues and bones, and it routinely requires multimodal information from CT and MRI series to identify its ambiguous tumor boundary. However, the conventional deep learning-based multimodal segmentation method suffers from limited prediction accuracy and frequently performs as well as or worse than single-modality segmentation models. The limited multimodal prediction performance indicates defective information extraction and integration from the input channels. This study aims to develop a 3D Gaussian-prompted Diffusion Model (3DG-PDM) for more clinically targeted information extraction and effective multimodal information integration, thereby facilitating more accurate and clinically interpretable GTV segmentation for NPC. Methods: We propose a 3D-Gaussian-Prompted Diffusion Model (3DGS-PDM) that operates NPC tumor contouring in multimodal clinical priors through a guided stepwise process. The proposed model contains two modules: a Gaussian Initialization Module that utilizes a 3D-Gaussian-Splatting technique to distill 3D-Gaussian representations based on clinical priors from CT, MRI-t2 and MRI-t1-contract-enhanced-fat-suppression (MRI-t1-cefs), respectively, and a Diffusion Segmentation Module that generates tumor segmentation step-by-step from the fused 3D-Gaussians prompts. We retrospectively collected data on 600 NPC patients from four hospitals through paired CT, MRI series and clinical GTV annotations, and divided that dataset into 480 training volumes and 120 testing volumes. Results: Our proposed method can achieve a mean dice similarity cofficient (DSC) of 84.29 ± 7.33, a mean average symmetric surface distance (ASSD) of 1.31 ± 0.63, and a 95th percentile of Hausdorff (HD₉₅) of 4.76 ± 1.98 on primary NPC tumor (GTVp) segmentation, and a DSC of 79.25 ± 10.01, an ASSD of 1.19 ± 0.72 and an HD₉₅ of 4.76 ± 1.71 on metastasis NPC tumor (GTVnd) segmentation. Comparative experiments further demonstrate that our method can significantly improve the multimodal segmentation performance on NPC tumors, with superior advantages over five other state-of-the-art comparative methods. Visual evaluation on the segmentation prediction process and a three-step ablation study on input channels further demonstrate the interpretability of our proposed method. Conclusions: This study proposes a performant and interpretable multimodal segmentation method for GTV of NPC, contributing greatly to precision improvement for NPC therapy treatment. Full article

(This article belongs to the Special Issue Image-Guided Adaptive Radiation Therapy (IGART): Advancing Precision Oncology)

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15 pages, 2884 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 5 | Viewed by 1785

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 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 KB

Open AccessArticle

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 1524

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 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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Review

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19 pages, 1049 KB

Open AccessReview

Image-Guided Adaptive Brachytherapy for Uterine Cancer: A Comprehensive Review

by Yi-Ching Chen and Chi-Yuan Yeh

Cancers 2026, 18(4), 693; https://doi.org/10.3390/cancers18040693 - 20 Feb 2026

Viewed by 742

Abstract

Background/Objectives: Image-guided adaptive brachytherapy (IGABT) has transformed the standard of care for locally advanced cervical cancer (LACC), enabling volumetric target definition and dose–volume histogram (DVH)-based planning to improve pelvic tumor control while limiting severe late toxicity. Methods: A comprehensive literature search of PubMed/MEDLINE and Embase was done for articles published up to August 2024, using combinations of the following keywords and Medical Subject Heading (MeSH) terms: “cervical cancer”, “endometrial cancer”, “vaginal cancer”, “uterine neoplasms”, “brachytherapy”, “high-dose-rate”, “image-guided”, “MRI-guided”, “3D brachytherapy”, “IGABT”, “interstitial”, “locoregional control”, “toxicity”, “quality of life”, and “patient-reported outcomes”. Results: We summarized the contemporary evidence on IGABT for cervical, endometrial, and primary or recurrent vaginal cancers, focusing on local control, survival, late morbidity, and patient-reported outcomes. We described the key target volume concepts (gross tumor volume, high- and intermediate-risk clinical target volumes), and the role of MRI-, CT-, and ultrasound-based planning with intracavitary, intracavitary–interstitial, and interstitial applicators. Conclusions: Image-guided adaptive brachytherapy has redefined the standard of care for the management of locally advanced cervical cancer. Through the integration of volumetric target concepts, DVH-based dose reporting, and advanced imaging, IGABT has enabled consistent dose escalation to the residual tumor while accounting for organ-at-risk constraints, resulting in high local control rates and reduced severe morbidity compared with historical 2D brachytherapy. Full article

(This article belongs to the Special Issue Image-Guided Adaptive Radiation Therapy (IGART): Advancing Precision Oncology)

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