Introduction: As part of French residents' radiotherapy training, delineation workstations were available at a national teaching course. We report a prospective comparative study of a non small cell lung cancer (NSCLC) case delineated by 120 residents before and after a radioanatomy/radiotherapy lecture.
Materials And Methods: The case of a patient with right upper lobe non small cell lung cancer (NSCLC) was provided for delineation to 32 groups of residents before and after a radiation therapy lecture about thoracic delineation. GTV, CTV and PTV was asked to each group. In a second step, the GTV, CTV and PTV were compared with those of 9 groups of senior physicians. Finally the consequences for treatment planning between each group before and after the course were explored.
Results: The expert's average GTV, CTV and PTV were 89.1 cm3, 242.3 cm3 and 293.9 cm3 respectively. For residents, those volumes were 103.4 cm3, 242.3 cm3 and 457.9 cm3 before teaching, compared to 99.5 cm3, 224.2 cm3 and 412.5 cm3 after teaching. The overlap (OV) and kappa (KI) indices before and after education were respectively 0.58 and 0.73. Compared to senior physicians, OV and KI indices were lower in the residents group (p = 0.039 and p = 0.043). An increased dose to the lung is noted for the residents' dosimetry compared to the experts' (V20: 23.2% versus 36.5%) due to the larger PTV delineated. No significant difference was observed for other organs at risk.
Conclusion: There were no significant differences for the delineation of the GTV and CTV before and after the course, although the differences tended to decrease after the course. The good initial quality of the contours could explain the lack of difference. V20 for lung was higher in the residents group compared to the experts group (23.2% vs 36.5%). No other treatment planning consequences were observed for other critical organs.
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http://dx.doi.org/10.1186/1748-717X-6-118 | DOI Listing |
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.
View Article and Find Full Text PDFJ Contemp Brachytherapy
June 2024
Division of Radiation Oncology, Department of Radiology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Purpose: The aim of this study was to investigate the predictive value of radiomic features of pre-treatment T2-weighted magnetic resonance images (MRI) for clinical outcomes of radiotherapy in cervical cancer patients.
Material And Methods: Ninety cervical cancer patients with stage IB-IVA were retrospectively analyzed. All patients received definitive radiotherapy with or without concurrent chemotherapy.
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