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| http://dx.doi.org/10.1186/s12904-022-00969-6 | DOI Listing |
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Clinical feasibility of Ethos auto-segmentation for adaptive whole-breast cancer treatment.
Front Oncol
December 2024
Radiotherapy Department, Montpellier Regional Cancer Institute, Montpellier, France.
Introduction: Following a preliminary work validating the technological feasibility of an adaptive workflow with Ethos for whole-breast cancer, this study aims to clinically evaluate the automatic segmentation generated by Ethos.
Material And Methods: Twenty patients initially treated on a TrueBeam accelerator for different breast cancer indications (right/left, lumpectomy/mastectomy) were replanned using the Ethos emulator. The adaptive workflow was performed using 5 randomly selected extended CBCTs per patient.
Personalized deep learning auto-segmentation models for adaptive fractionated magnetic resonance-guided radiation therapy of the abdomen.
Med Phys
December 2024
Department of Radiation Oncology, LMU University Hospital, LMU Munich, Munich, Germany.
Background: Manual contour corrections during fractionated magnetic resonance (MR)-guided radiotherapy (MRgRT) are time-consuming. Conventional population models for deep learning auto-segmentation might be suboptimal for MRgRT at MR-Linacs since they do not incorporate manual segmentation from treatment planning and previous fractions.
Purpose: In this work, we investigate patient-specific (PS) auto-segmentation methods leveraging expert-segmented planning and prior fraction MR images (MRIs) to improve auto-segmentation on consecutive treatment days.
Prior Knowledge-Guided U-Net for Automatic CTV Segmentation in Postmastectomy Radiotherapy of Breast Cancer.
Int J Radiat Oncol Biol Phys
December 2024
Department of Radiation Oncology, Peking University Third Hospital, Beijing 100191, China. Electronic address:
Purpose: This study aimed to design and evaluate a prior-knowledge-guided U-Net (PK-UNet) for automatic clinical target volume (CTV) segmentation in postmastectomy radiotherapy for breast cancer.
Methods And Materials: A total of 102 computed tomography (CT) scans from breast cancer patients who underwent postmastectomy were retrospectively collected. Of these, 80 scans were used for training with 5-fold cross-validation, and 22 scans for independent testing.
Assessing Large Language Models for Oncology Data Inference From Radiology Reports.
JCO Clin Cancer Inform
December 2024
Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA.
Purpose: We examined the effectiveness of proprietary and open large language models (LLMs) in detecting disease presence, location, and treatment response in pancreatic cancer from radiology reports.
Methods: We analyzed 203 deidentified radiology reports, manually annotated for disease status, location, and indeterminate nodules needing follow-up. Using generative pre-trained transformer (GPT)-4, GPT-3.
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