Novel Imaging Techniques in Radiotherapy

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 303

Special Issue Editors


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Guest Editor
Department of Biomedical Engineering, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Interests: biomedical optics; optical imaging and tomography; image-guided radiotherapy

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Guest Editor
Department of Radiation Oncology, Sylvester Comprehensive Cancer Center/Miller School of Medicine, University of Miami, Miami, FL, USA
Interests: molecular imaging; nano cancer therapy

Special Issue Information

Dear Colleagues,

Radiotherapy (RT) remains one of the most effective cancer treatment modalities. Its success depends on precise tumor localization, treatment planning, and assessment. Contemporary medical imaging provides complementary information to support radiation therapy (RT). For example, CT and MRI offer precise anatomical and tumor localization for image-guided RT. In contrast, nuclear medicine imaging modalities such as PET and SPECT are particularly valuable for metabolic imaging, enabling highly sensitive and specific tumor detection. These techniques provide functional information reflecting tumor metabolism, which is critical for monitoring treatment response. Cherenkov imaging, an emerging technique, has shown promise for real-time in vivo dosimetry monitoring. Recent innovations in AI-assisted imaging technologies have further enhanced RT by improving target accuracy, enabling adaptive image-guided approaches. Collectively, these advanced imaging techniques increase RT efficacy by minimizing radiation exposure to healthy tissues and reducing treatment-related toxicity.

Many radiobiological mechanisms critical to RT progress remain unclear. To address this, researchers in radiobiology have developed various tumor models for RT research. To accurately deliver radiation to tumors, several research groups have developed preclinical small-animal irradiators that mimic clinical RT. CT, MRI, and PET have been adapted to guide irradiation, while emerging techniques, such as optical, photoacoustic, and ionizing-radiation acoustic imaging, are revolutionizing the field. These cutting-edge modalities offer high-resolution, functional, and molecular imaging capabilities, allowing tumor visualization for irradiation guidance and radiation response assessment, ultimately enhancing reproducibility of radiobiological discoveries.

This Special Issue focuses on innovative imaging approaches—clinical and preclinical—to improve RT precision and outcomes. We invite researchers to contribute original research and technical advancements in this field.

Dr. Zijian Deng
Dr. Junwei Shi
Guest Editors

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Keywords

  • radiotherapy
  • imaging
  • tumor localization
  • treatment assessment
  • radiation toxicity

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Published Papers (1 paper)

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Research

16 pages, 2032 KiB  
Article
Auto-Segmentation and Auto-Planning in Automated Radiotherapy for Prostate Cancer
by Sijuan Huang, Jingheng Wu, Xi Lin, Guangyu Wang, Ting Song, Li Chen, Lecheng Jia, Qian Cao, Ruiqi Liu, Yang Liu, Xin Yang, Xiaoyan Huang and Liru He
Bioengineering 2025, 12(6), 620; https://doi.org/10.3390/bioengineering12060620 - 6 Jun 2025
Viewed by 171
Abstract
Objective: The objective of this study was to develop and assess the clinical feasibility of auto-segmentation and auto-planning methodologies for automated radiotherapy in prostate cancer. Methods: A total of 166 patients were used to train a 3D Unet model for segmentation of [...] Read more.
Objective: The objective of this study was to develop and assess the clinical feasibility of auto-segmentation and auto-planning methodologies for automated radiotherapy in prostate cancer. Methods: A total of 166 patients were used to train a 3D Unet model for segmentation of the gross tumor volume (GTV), clinical tumor volume (CTV), nodal CTV (CTVnd), and organs at risk (OARs). Performance was assessed by the Dice similarity coefficient (DSC), the Recall, Precision, Volume Ratio (VR), the 95% Hausdorff distance (HD95%), and the volumetric revision degree (VRD). An auto-planning network based on a 3D Unet was trained on 77 treatment plans derived from the 166 patients. Dosimetric differences and clinical acceptability of the auto-plans were studied. The effect of OAR editing on dosimetry was also evaluated. Results: On an independent set of 50 cases, the auto-segmentation process took 1 min 20 s per case. The DSCs for GTV, CTV, and CTVnd were 0.87, 0.88, and 0.82, respectively, with VRDs ranging from 0.09 to 0.14. The segmentation of OARs demonstrated high accuracy (DSC ≥ 0.83, Recall/Precision ≈ 1.0). The auto-planning process required 1–3 optimization iterations for 50%, 40%, and 10% of cases, respectively, and exhibited significant better conformity (p ≤ 0.01) and OAR sparing (p ≤ 0.03) while maintaining comparable target coverage. Only 6.7% of auto-plans were deemed unacceptable compared to 20% of manual plans, with 75% of auto-plans considered superior. Notably, the editing of OARs had no significant impact on doses. Conclusions: The accuracy of auto-segmentation is comparable to that of manual segmentation, and the auto-planning offers equivalent or better OAR protection, meeting the requirements of online automated radiotherapy and facilitating its clinical application. Full article
(This article belongs to the Special Issue Novel Imaging Techniques in Radiotherapy)
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