AIMC Topic: Radiotherapy Dosage
Clear Filters Showing 121 to 130 of 497 articles
Combined deep learning and radiomics in pretreatment radiation esophagitis prediction for patients with esophageal cancer underwent volumetric modulated arc therapy.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jul 14, 2024
PURPOSE: To develop a combined radiomics and deep learning (DL) model in predicting radiation esophagitis (RE) of a grade ≥ 2 for patients with esophageal cancer (EC) underwent volumetric modulated arc therapy (VMAT) based on computed tomography (CT)...
Proton spot dose estimation based on positron activity distributions with neural network.
Medical physics
Jul 10, 2024
BACKGROUND: Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently...
Patient-specific deep learning for 3D protoacoustic image reconstruction and dose verification in proton therapy.
Medical physics
Jul 9, 2024
BACKGROUND: Protoacoustic (PA) imaging has the potential to provide real-time 3D dose verification of proton therapy. However, PA images are susceptible to severe distortion due to limited angle acquisition. Our previous studies showed the potential ...
Synthetic CT generation for pelvic cases based on deep learning in multi-center datasets.
Radiation oncology (London, England)
Jul 9, 2024
BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy.
Localized fine-tuning and clinical evaluation of deep-learning based auto-segmentation (DLAS) model for clinical target volume (CTV) and organs-at-risk (OAR) in rectal cancer radiotherapy.
Radiation oncology (London, England)
Jul 2, 2024
BACKGROUND AND PURPOSE: Various deep learning auto-segmentation (DLAS) models have been proposed, some of which have been commercialized. However, the issue of performance degradation is notable when pretrained models are deployed in the clinic. This...
Development of a risk prediction model for radiation dermatitis following proton radiotherapy in head and neck cancer using ensemble machine learning.
Radiation oncology (London, England)
Jun 24, 2024
PURPOSE: This study aims to develop an ensemble machine learning-based (EML-based) risk prediction model for radiation dermatitis (RD) in patients with head and neck cancer undergoing proton radiotherapy, with the goal of achieving superior predictiv...
From plan to delivery: Machine learning based positional accuracy prediction of multi-leaf collimator and estimation of delivery effect in volumetric modulated arc therapy.
Journal of applied clinical medical physics
Jun 20, 2024
PURPOSE: The positional accuracy of MLC is an important element in establishing the exact dosimetry in VMAT. We comprehensively analyzed factors that may affect MLC positional accuracy in VMAT, and constructed a model to predict MLC positional deviat...
Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy.
Computer methods and programs in biomedicine
Jun 19, 2024
BACKGROUND AND OBJECTIVE: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiother...
Neural network dose prediction for cervical brachytherapy: Overcoming data scarcity for applicator-specific models.
Medical physics
May 30, 2024
BACKGROUND: 3D neural network dose predictions are useful for automating brachytherapy (BT) treatment planning for cervical cancer. Cervical BT can be delivered with numerous applicators, which necessitates developing models that generalize to multip...
Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.
Physics in medicine and biology
May 30, 2024
To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.Two 3D UNets were established to predict ph...
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