AIMC Topic: Radiotherapy

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System for High-Intensity Evaluation During Radiation Therapy (SHIELD-RT): A Prospective Randomized Study of Machine Learning-Directed Clinical Evaluations During Radiation and Chemoradiation.

Journal of clinical oncology : official journal of the American Society of Clinical Oncology
PURPOSE: Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited...

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning.

International journal of radiation oncology, biology, physics
PURPOSE: This study aims to evaluate the impact of key parameters on the pseudo computed tomography (pCT) quality generated from magnetic resonance imaging (MRI) with a 3-dimensional (3D) convolutional neural network.

Radiomics and deep learning in lung cancer.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomograp...

High quality proton portal imaging using deep learning for proton radiation therapy: a phantom study.

Biomedical physics & engineering express
Purpose; For shoot-through proton treatments, like FLASH radiotherapy, there will be protons exiting the patient which can be used for proton portal imaging (PPI), revealing valuable information for the validation of tumor location in the beam's-eye-...

Artificial Intelligence in radiotherapy: state of the art and future directions.

Medical oncology (Northwood, London, England)
Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application a...

Multi-task learning for the segmentation of organs at risk with label dependence.

Medical image analysis
Automatic segmentation of organs at risk is crucial to aid diagnoses and remains a challenging task in medical image analysis domain. To perform the segmentation, we use multi-task learning (MTL) to accurately determine the contour of organs at risk ...

A novel deep learning model using dosimetric and clinical information for grade 4 radiotherapy-induced lymphopenia prediction.

Physics in medicine and biology
Radiotherapy-induced lymphopenia has increasingly been shown to reduce cancer survivorship. We developed a novel hybrid deep learning model to efficiently integrate an entire set of dosimetric parameters of a radiation treatment plan with a patient's...

An artificial neural network to model response of a radiotherapy beam monitoring system.

Medical physics
PURPOSE: The integral quality monitor (IQM) is a real-time radiotherapy beam monitoring system, which consists of a spatially sensitive large-area ion chamber, mounted at the collimator of the linear accelerator (linac), and a calculation algorithm t...

Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer.

Radiation oncology (London, England)
BACKGROUND: Accurate and standardized descriptions of organs at risk (OARs) are essential in radiation therapy for treatment planning and evaluation. Traditionally, physicians have contoured patient images manually, which, is time-consuming and subje...

Patient-specific reconstruction of volumetric computed tomography images from a single projection view via deep learning.

Nature biomedical engineering
Tomographic imaging using penetrating waves generates cross-sectional views of the internal anatomy of a living subject. For artefact-free volumetric imaging, projection views from a large number of angular positions are required. Here we show that a...