Functional dose-volume histograms are proposed as an extension of the conventional dose-volume histograms, for quantitative assessment of three-dimensional radiation dose coverage of functionally heterogeneous normal organs. Examples are given to illustrate possible applications of this approach to the treatment of a brain tumour or a lung tumour, in which cases the distribution of the normal organ function can be obtained from functional dose-volume modalities. It is shown that a significant difference exists between the functional dose-volume histograms and the conventional dose-volume histograms when the normal organ function is non-uniformly distributed within the organ. Utilization of functional dose-volume histograms as the input for the calculation of normal tissue complication probabilities is discussed for different normal tissue structures.
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http://dx.doi.org/10.1088/0031-9155/42/2/007 | DOI Listing |
Cureus
December 2024
Physics and Engineering, London Regional Cancer Program, London, CAN.
Introduction: Radiation may unintentionally injure myocardial tissue, potentially leading to radiation-induced cardiac disease (RICD), with the net benefit of non-small cell lung cancer (NSCLC) radiotherapy (RT) due to the proximity of the lung and heart. RTOG-0617 showed a greater reduction in overall survival (OS) comparing higher doses to standard radiation doses in NSCLC RT. VHeart has been reported as an OS predictor in the first- and fifth-year follow-ups.
View Article and Find Full Text PDFDiagnostics (Basel)
January 2025
Institute of Information Technology, Vietnam Academy of Science and Technology, Hoang Quoc Viet, Hanoi 10072, Vietnam.
: Cancer is a highly lethal disease with a significantly high mortality rate. One of the most commonly used methods for treatment is radiation therapy. However, cancer treatment using radiotherapy is a time-consuming process that requires significant manual work from planners and doctors.
View Article and Find Full Text PDFFront Oncol
January 2025
Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, Republic of Korea.
Purpose: Recent deep-learning based synthetic computed tomography (sCT) generation using magnetic resonance (MR) images have shown promising results. However, generating sCT for the abdominal region poses challenges due to the patient motion, including respiration and peristalsis. To address these challenges, this study investigated an unsupervised learning approach using a transformer-based cycle-GAN with structure-preserving loss for abdominal cancer patients.
View Article and Find Full Text PDFPhys Med
January 2025
Medical Physics Dept IRCCS San Raffaele Scientific Institution Milano Italy.
Purpose: To train and validate KB prediction models by merging a large multi-institutional cohort of whole breast irradiation (WBI) plans using tangential fields.
Methods: Ten institutions (INST1-INST10, 1481 patients) developed their KB-institutional models for left/right WBI (ten models for right and eight models for left). The transferability of models among centers was assessed based on the overlap of the geometric Principal Component (PC1) of each model when applied to other institutions and/or on the presence of significantly different optimization policies.
Phys Med Biol
January 2025
Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, Downs Road, Sutton, London, Surrey, SM2 5PT, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
This study aims to develop and evaluate a fast and robust deep learningbased auto-segmentation approach for organs at risk in MRI-guided radiotherapy of pancreatic cancer to overcome the problems of time-intensive manual contouring in online adaptive workflows. The research focuses on implementing novel data augmentation techniques to address the challenges posed by limited datasets. Approach: This study was conducted in two phases.
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