AIMC Topic: Radiotherapy Dosage
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Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment.
Physics in medicine and biology
Apr 30, 2024
Monte Carlo (MC) simulations are the benchmark for accurate radiotherapy dose calculations, notably in patient-specific high dose rate brachytherapy (HDR BT), in cases where considering tissue heterogeneities is critical. However, the lengthy computa...
A deep learning-based 3D Prompt-nnUnet model for automatic segmentation in brachytherapy of postoperative endometrial carcinoma.
Journal of applied clinical medical physics
Apr 29, 2024
PURPOSE: To create and evaluate a three-dimensional (3D) Prompt-nnUnet module that utilizes the prompts-based model combined with 3D nnUnet for producing the rapid and consistent autosegmentation of high-risk clinical target volume (HR CTV) and organ...
Clinical VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning.
Medical physics
Apr 26, 2024
BACKGROUND: Volumetric modulated arc therapy (VMAT) machine parameter optimization (MPO) remains computationally expensive and sensitive to input dose objectives creating challenges for manual and automatic planning. Reinforcement learning (RL) invol...
Dosimetric impact of contour editing on CT and MRI deep-learning autosegmentation for brain OARs.
Journal of applied clinical medical physics
Apr 25, 2024
PURPOSE: To establish the clinical applicability of deep-learning organ-at-risk autocontouring models (DL-AC) for brain radiotherapy. The dosimetric impact of contour editing, prior to model training, on performance was evaluated for both CT and MRI-...
In vivo EPID-based daily treatment error identification for volumetric-modulated arc therapy in head and neck cancers with a hierarchical convolutional neural network: a feasibility study.
Physical and engineering sciences in medicine
Apr 22, 2024
We proposed a deep learning approach to classify various error types in daily VMAT treatment of head and neck cancer patients based on EPID dosimetry, which could provide additional information to support clinical decisions for adaptive planning. 146...
A deep learning-based method for the prediction of temporal lobe injury in patients with nasopharyngeal carcinoma.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Apr 22, 2024
PURPOSE: To establish a deep learning-based model to predict radiotherapy-induced temporal lobe injury (TLI).
An artificial neural network based approach for predicting the proton beam spot dosimetric characteristics of a pencil beam scanning technique.
Biomedical physics & engineering express
Apr 22, 2024
Utilising Machine Learning (ML) models to predict dosimetric parameters in pencil beam scanning proton therapy presents a promising and practical approach. The study developed Artificial Neural Network (ANN) models to predict proton beam spot size an...
Deep learning-based optimization of field geometry for total marrow irradiation delivered with volumetric modulated arc therapy.
Medical physics
Apr 18, 2024
BACKGROUND: Total marrow (lymphoid) irradiation (TMI/TMLI) is a radiotherapy treatment used to selectively target the bone marrow and lymph nodes in conditioning regimens for allogeneic hematopoietic stem cell transplantation. A complex field geometr...
Predicting treatment plan approval probability for high-dose-rate brachytherapy of cervical cancer using adversarial deep learning.
Physics in medicine and biology
Apr 17, 2024
Predicting the probability of having the plan approved by the physician is important for automatic treatment planning. Driven by the mathematical foundation of deep learning that can use a deep neural network to represent functions accurately and fle...
Dose-Incorporated Deep Ensemble Learning for Improving Brain Metastasis Stereotactic Radiosurgery Outcome Prediction.
International journal of radiation oncology, biology, physics
Apr 12, 2024
PURPOSE: To develop a novel deep ensemble learning model for accurate prediction of brain metastasis (BM) local control outcomes after stereotactic radiosurgery (SRS).
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