Medical Imaging and Application in Radiotherapy

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (20 March 2023) | Viewed by 3578

Special Issue Editor


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Guest Editor
Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, No. 123, Dapei Rd., Niaosong Dist., Kaohsiung City 833, Taiwan
Interests: radiotherapy; intensity-modulated radiotherapy; IMRT; radiation oncology; radiation therapy; stereotactic radiosurgery; radiosurgery; radiation biology
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Special Issue Information

Dear Colleagues,

Since 1895, with the discovery of radiation by Roentgen, it has been used to treat diseases. Up to now, radiation has been one of the major modalities to treat cancer. Radiation therapy has developed from the treatment field and was drawn on a plane film to computerized 3-dimensional volumetric treatment plan. Advances in medical imaging have played an indispensable role. High-quality radiation therapy relies on precise localization to deliver radiation within the body and accurate radiation dose calculation. The localization of the lesion for treatment depends on delicate medical imaging for geometric information, and electron density of tissues for radiation dose calculation. Image guidance is also an important issue when the patient lies on a couch for treatment. These are all about medical imaging for radiation therapy.

This Special Issue is proposed to reveal the medical imaging and application in radiotherapy. Architectures may include, but are not limited to, medical imaging applications for cancer treatment, novel concurrent algorithms and applications for radiation therapy dose calculation, medical imaging for treatment target delineation, radiation therapy treatment plan optimization, image guidance, treatment response evaluation by medical imaging, respiratory motion in medical imaging, and anything about radiation therapy. Artificial intelligence applications, computer assistance, and quality assurance in radiation therapy are also topics of interest.

This Special Issue will publish high-quality, original research papers in the overlapping fields of:

  • Medical imaging;
  • Radiation therapy;
  • Target delineation;
  • Medical image registration;
  • Radiation dose calculation;
  • Radiation treatment planning optimization;
  • Image guidance;
  • Respiratory control;
  • Treatment response by medical imaging;
  • Computer assistance radiotherapy;
  • Quality assurance in radiotherapy.

Dr. Yujie Huang
Guest Editor

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Keywords

  • medical imaging
  • radiation
  • radiotherapy
  • target delineation
  • image registration
  • radiation dose calculation
  • optimization
  • image guidance
  • respiratory control
  • treatment response
  • quality assurance
  • computer assistance

Published Papers (2 papers)

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Research

13 pages, 1938 KiB  
Article
Textural and Conventional Pretherapeutic [18F]FDG PET/CT Parameters for Survival Outcome Prediction in Stage III and IV Oropharyngeal Cancer Patients
by David Palomino-Fernández, Eva Milara, Álvaro Galiana, Miguel Sánchez-Ortiz, Alexander P. Seiffert, Justino Jiménez-Almonacid, Adolfo Gómez-Grande, Sebastián Ruiz-Solís, Ana Ruiz-Alonso, Enrique J. Gómez, María José Tabuenca and Patricia Sánchez-González
Appl. Sci. 2024, 14(4), 1454; https://doi.org/10.3390/app14041454 - 10 Feb 2024
Viewed by 600
Abstract
Evidence is emerging about the value of textural features as powerful outcome predictors in cancer lesions. The aim of this study is to evaluate the potential of [18F]FDG PET/CT conventional and textural parameters as survival predictors in patients with stage III [...] Read more.
Evidence is emerging about the value of textural features as powerful outcome predictors in cancer lesions. The aim of this study is to evaluate the potential of [18F]FDG PET/CT conventional and textural parameters as survival predictors in patients with stage III and IV oropharyngeal cancer. The database includes 39 patients. Segmentation of the primary lesions was performed. A total of 48 features were extracted, comprising conventional parameters and textural features. A 2-year follow-up period to analyze the Overall Survival (OS) and Relapse-Free Survival (RFS) rates was defined. Kaplan–Meier and Cox proportional hazards regression analyses were computed. Higher TLG (p = 0.001) and Surface (p = 0.001) are significantly related to better OS in Cox regression analysis after multiple-testing correction. Higher GLZLM_ZLNU (p = 0.001) is significantly related to greater relapse rates in RFS Kaplan–Meier analysis after multiple-testing correction. Quantitative [18F]FDG PET/CT image features, especially the TLG, have been confirmed as predictors of OS and RFS. Textural features, such as GLZLM_ZLNU, demonstrated a potential predictive value for the OS and RFS of the patients. RFS analysis suggest stabilization of patients adhering to the treatment, showing no relapse events after 20 months of follow-up. [18F]FDG PET/CT is a useful tool for predicting prognosis after chemoradiation therapy of oropharyngeal cancer patients. Full article
(This article belongs to the Special Issue Medical Imaging and Application in Radiotherapy)
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14 pages, 6418 KiB  
Article
Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring for Head and Neck Radiation Therapy
by Curtise K. C. Ng, Vincent W. S. Leung and Rico H. M. Hung
Appl. Sci. 2022, 12(22), 11681; https://doi.org/10.3390/app122211681 - 17 Nov 2022
Cited by 9 | Viewed by 2343
Abstract
Various commercial auto-contouring solutions have emerged over past few years to address labor-intensiveness, and inter- and intra-operator variabilities issues of traditional manual anatomy contouring for head and neck (H&N) radiation therapy (RT). The purpose of this study is to compare the clinical performances [...] Read more.
Various commercial auto-contouring solutions have emerged over past few years to address labor-intensiveness, and inter- and intra-operator variabilities issues of traditional manual anatomy contouring for head and neck (H&N) radiation therapy (RT). The purpose of this study is to compare the clinical performances between RaySearch Laboratories deep learning (DL) and atlas-based auto-contouring tools for organs at risk (OARs) segmentation in the H&N RT with the manual contouring as reference. Forty-five H&N computed tomography datasets were used for the DL and atlas-based auto-contouring tools to contour 16 OARs and time required for the segmentation was measured. Dice similarity coefficient (DSC), Hausdorff distance (HD) and HD 95th-percentile (HD95) were used to evaluate geometric accuracy of OARs contoured by the DL and atlas-based auto-contouring tools. Paired sample t-test was employed to compare the mean DSC, HD, HD95, and contouring time values of the two groups. The DL auto-contouring approach achieved more consistent performance in OARs segmentation than its atlas-based approach, resulting in statistically significant time reduction of the whole segmentation process by 40% (p < 0.001). The DL auto-contouring had statistically significantly higher mean DSC and lower HD and HD95 values (p < 0.001–0.009) for 10 out of 16 OARs. This study proves that the RaySearch Laboratories DL auto-contouring tool has significantly better clinical performances than its atlas-based approach. Full article
(This article belongs to the Special Issue Medical Imaging and Application in Radiotherapy)
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