Breakthroughs in Radiotherapy for Cancer

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Radiobiology and Nuclear Medicine".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 1530

Special Issue Editors

Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
Interests: image guided radiotherapy; medical image analysis; smart radiotherapy biomaterial
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Guest Editor
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, MD 21287, USA
Interests: precision medicine in radiation oncology; image-guided interventions; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Radiography has been an integral part of cancer management. In recent years, the emergence of new modalities, including bio-guided radiotherapy, radioimmunotherapy, and drug radiotherapy, as well as artificial intelligence-enhanced methods, has changed the landscape of radiotherapy. New modalities, such as FLASH therapy, have shown promising improvements compared to traditional radiotherapy. Bio-guided radiotherapy is a more targeted form of radiotherapy treatment using the tumor itself and incorporating positron emission tomography (PET) into the procedure. Radioimmunotherapy targets tumor antigens using monoclonal antibodies in conjunction with isotopes, offering personalized, systemic, and durable treatment. Drug radiotherapy, which integrates biological and imaging markers, personalizes treatment, thereby enhancing the therapeutic index of conventional therapy. In addition to these advances, artificial intelligence is applied to various aspects of radiotherapy to increase the effectiveness of treatment. Multi-modal AI systems can incorporate electronic health records, such as physicians’ reports, into different stages of the treatment workflow, reducing the time to treatment and making it more effective.

This Special Issue aims to capture the latest advances in all aspects of radiotherapy and provide a resource for those studying the latest advancements in cancer treatment.

Dr. Kai Ding
Dr. Hamed Hooshangnejad
Guest Editors

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Keywords

  • radiotherapy
  • radioimmunotherapy
  • AI-enhanced radiotherapy
  • bio-guided radiotherapy

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Published Papers (1 paper)

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Research

14 pages, 44941 KB  
Article
Multiscale Segmentation-Guided Diffusion Model for CBCT-to-CT Synthesis
by Yike Guo, Yi Luo, Hamed Hooshangnejad, Rui Zhang, Xue Feng, Quan Chen, Wilfred Ngwa and Kai Ding
Life 2025, 15(12), 1871; https://doi.org/10.3390/life15121871 - 6 Dec 2025
Viewed by 1277
Abstract
To improve synthetic CT (sCT) generation from cone-beam CT (CBCT) in radiotherapy, we propose a multiscale segmentation-guided diffusion framework. The proposed model integrates anatomical priors across multiple spatial resolutions through a segmentation mask pyramid and introduces a scale-specific loss function to guide learning [...] Read more.
To improve synthetic CT (sCT) generation from cone-beam CT (CBCT) in radiotherapy, we propose a multiscale segmentation-guided diffusion framework. The proposed model integrates anatomical priors across multiple spatial resolutions through a segmentation mask pyramid and introduces a scale-specific loss function to guide learning at each level. When evaluated on the SynthRAD2023 brain dataset, our model achieves a mean absolute error (MAE) of 61.82 HU, a peak signal-to-noise ratio (PSNR) of 32.05 dB, and a structural similarity index (SSIM) of 0.90, outperforming baseline models. These results suggest that multiscale anatomical guidance can improve the fidelity and anatomical consistency of sCT images, thus facilitating high-quality CBCT-to-CT translation in radiotherapy applications. Full article
(This article belongs to the Special Issue Breakthroughs in Radiotherapy for Cancer)
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