AIMC Topic: Radiotherapy, Intensity-Modulated
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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...
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...
Multi-center Dose Prediction Using Attention-aware Deep learning Algorithm Based on Transformers for Cervical Cancer Radiotherapy.
Clinical oncology (Royal College of Radiologists (Great Britain))
Mar 26, 2024
AIMS: Accurate dose delivery is crucial for cervical cancer volumetric modulated arc therapy (VMAT). We aimed to develop a robust deep-learning (DL) algorithm for fast and accurate dose prediction of cervical cancer VMAT in multicenter datasets and t...
"sCT-Feasibility" - a feasibility study for deep learning-based MRI-only brain radiotherapy.
Radiation oncology (London, England)
Mar 8, 2024
BACKGROUND: Radiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images are used for RT treatment planning whereas magnetic resonance imaging (MRI) images are used for tumo...
Assessment of lymph node area coverage with total marrow irradiation and implementation of total marrow and lymphoid irradiation using automated deep learning-based segmentation.
PloS one
Mar 8, 2024
BACKGROUND: Total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI) have the advantages. However, delineating target lesions according to TMI and TMLI plans is labor-intensive and time-consuming. In addition, although the deli...
Evaluation of the performance of both machine learning models using PET and CT radiomics for predicting recurrence following lung stereotactic body radiation therapy: A single-institutional study.
Journal of applied clinical medical physics
Mar 4, 2024
PURPOSE: Predicting recurrence following stereotactic body radiotherapy (SBRT) for non-small cell lung cancer provides important information for the feasibility of the individualized radiotherapy and allows to select the appropriate treatment strateg...
Radiosurgery
Radiotherapy Planning, Computer-Assisted
Radiotherapy Dosage
Radiotherapy, Intensity-Modulated
Middle Aged
Image Processing, Computer-Assisted
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Positron Emission Tomography Computed Tomography
Neoplasm Recurrence, Local
Aged, 80 and over
Algorithms
Humans
Radiomics
Prognosis
Retrospective Studies
Machine Learning
Male
Female
Aged
Gross failure rates and failure modes for a commercial AI-based auto-segmentation algorithm in head and neck cancer patients.
Journal of applied clinical medical physics
Jan 23, 2024
PURPOSE: Artificial intelligence (AI) based commercial software can be used to automatically delineate organs at risk (OAR), with potential for efficiency savings in the radiotherapy treatment planning pathway, and reduction of inter- and intra-obser...
A 3D transfer learning approach for identifying multiple simultaneous errors during radiotherapy.
Physics in medicine and biology
Jan 17, 2024
. Deep learning models, such as convolutional neural networks (CNNs), can take full dose comparison images as input and have shown promising results for error identification during treatment. Clinically, complex scenarios should be considered, with t...
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