Background And Purpose: Brachytherapy (BT) plays a crucial role in the management of invasive cervix cancer from stage I to IV. Intracavitary techniques are based on afterloading devices, with different types of applicators. CT and/or MRI compatible applicators allow a sectional image based approach with a better assessment of gross tumour volume (GTV) and definition and delineation of target volume (CTV) compared to traditional approaches. Accurate and reproducible delineation of GTV, CTV and PTV, as well as of critical organs has a direct impact on BT treatment planning, especially if it is possible to adapt the pear-shape isodose by optimisation using DVH analysis. When introducing a 3D image based approach for GTV and CTV assessment, there is a need for a common language to describe the concepts and to define the terms which are to be used.
Methods: In 2000, GEC-ESTRO decided to support 3D imaging based 3D treatment planning approach in cervix cancer BT with the creation of a Working Group. The task was to describe basic concepts and terms and to work out a terminology enabling various groups working in this advanced field to use a common language. The recommendations described in this report were proposed based on clinical experience and dosimetric concepts of different institutions (IGR, Leuven, Vienna) and were stepwise validated against the background of different clinical experience.
Conclusions: As GTV and CTV for BT change significantly during treatment, time frame for assessment of GTV and CTV for BT is specified in this report: at time of diagnosis GTV(D), CTV(D) and at time of BT GTV(B), CTV(B). Furthermore, CTV for BT is defined related to risk for recurrence: high risk CTV and intermediate risk CTV. Beside verbal descriptions detailed examples are given, partly in form of schematic drawings.
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http://dx.doi.org/10.1016/j.radonc.2004.12.015 | DOI Listing |
Int J Radiat Oncol Biol Phys
January 2025
Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands.
Purpose: Deep learning is a promising approach to increase reproducibility and time-efficiency of GTV delineation in head and neck cancer, but model evaluation primarily relies on manual GTV delineations as reference annotation, which are subjective and tend to overestimate tumor volume. This study aimed to validate a deep learning model for laryngeal and hypopharyngeal GTV segmentation with pathology and to compare its performance with clinicians' manual delineations.
Materials And Methods: A retrospective dataset of 193 laryngeal and hypopharyngeal cancer patients was used to train a deep learning model with clinical GTV delineations as reference.
Radiother Oncol
January 2025
Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA; Medical Artificial Intelligence and Automation Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX, USA. Electronic address:
Background And Purpose: Daily online adaptive radiotherapy (DART) increases treatment accuracy by crafting daily customized plans that adjust to the patient's daily setup and anatomy. The routine application of DART is limited by its resource-intensive processes. This study proposes a novel DART strategy for head and neck squamous cell carcinoma (HNSCC), automizing the process by propagating physician-edited treatment contours for each fraction.
View Article and Find Full Text PDFPhys Med
January 2025
Division of Medical Radiation Physics, Department of Physics, Stockholm University, Stockholm, Sweden; Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden.
Purpose: We investigate the feasibility of using a biophysically guided approach for delineating the Clinical Target Volume (CTV) in Glioblastoma Multiforme (GBM) by evaluating its impact on the treatment outcomes, specifically Overall Survival (OS) time.
Methods: An established reaction-diffusion model was employed to simulate the spatiotemporal evolution of cancerous regions in T1-MRI images of GBM patients. The effects of the parameters of this model on the simulated tumor borders were quantified and the optimal values were used to estimate the distribution of infiltrative cells (CTVmodel).
Phys Imaging Radiat Oncol
October 2024
Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
Purpose: Multiparametric magnetic resonance imaging (MRI) is known to provide predictors for malignancy and treatment outcome. The inclusion of these datasets in workflows for online adaptive planning remains under investigation. We demonstrate the feasibility of longitudinal relaxometry in online MR-guided adaptive stereotactic body radiotherapy (SBRT) to the prostate and dominant intra-prostatic lesion (DIL).
View Article and Find Full Text PDFBMC Med Imaging
December 2024
Laboratory of Image Science and Technology, Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Ministry of Education, Southeast University, Sipailou 2, Nanjing, P.R. China.
Purpose: The segmentation of target volume and organs at risk (OAR) was a significant part of radiotherapy. Specifically, determining the location and scale of the esophagus in simulated computed tomography images was difficult and time-consuming primarily due to its complex structure and low contrast with the surrounding tissues. In this study, an Enhanced Cross-stage-attention U-Net was proposed to solve the segmentation problem for the esophageal gross tumor volume (GTV) and clinical tumor volume (CTV) in CT images.
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