AIMC Topic: Radiotherapy Planning, Computer-Assisted

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Development of deep learning-based novel auto-segmentation for the prostatic urethra on planning CT images for prostate cancer radiotherapy.

Radiological physics and technology
Urinary toxicities are one of the serious complications of radiotherapy for prostate cancer, and dose-volume histogram of prostatic urethra has been associated with such toxicities in previous reports. Previous research has focused on estimating the ...

Automatic segmentation of high-risk clinical target volume and organs at risk in brachytherapy of cervical cancer with a convolutional neural network.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: This study aimed to design an autodelineation model based on convolutional neural networks for generating high-risk clinical target volumes and organs at risk in image-guided adaptive brachytherapy for cervical cancer.

Artificial intelligence for treatment delivery: image-guided radiotherapy.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is...

Artificial Intelligence for Radiation Treatment Planning: Bridging Gaps From Retrospective Promise to Clinical Reality.

Clinical oncology (Royal College of Radiologists (Great Britain))
Artificial intelligence (AI) radiation therapy (RT) planning holds promise for enhancing the consistency and efficiency of the RT planning process. Despite technical advancements, the widespread integration of AI into RT treatment planning faces chal...

A deep-learning-based surrogate model for Monte-Carlo simulations of the linear energy transfer in primary brain tumor patients treated with proton-beam radiotherapy.

Physics in medicine and biology
This study explores the use of neural networks (NNs) as surrogate models for Monte-Carlo (MC) simulations in predicting the dose-averaged linear energy transfer (LET) of protons in proton-beam therapy based on the planned dose distribution and patien...

Deep learning applied to dose prediction in external radiation therapy: A narrative review.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machin...

Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

Journal of applied clinical medical physics
BACKGROUND: Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed fo...

A multicenter study on deep learning for glioblastoma auto-segmentation with prior knowledge in multimodal imaging.

Cancer science
A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor-intensive and heavily reliant on the experience of radiation oncologis...

Deep learning for autosegmentation for radiotherapy treatment planning: State-of-the-art and novel perspectives.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]
The rapid development of artificial intelligence (AI) has gained importance, with many tools already entering our daily lives. The medical field of radiation oncology is also subject to this development, with AI entering all steps of the patient jour...

Deep learning-based dose prediction for magnetic resonance-guided prostate radiotherapy.

Medical physics
BACKGROUND: Daily adaptive radiotherapy, as performed with the Elekta Unity MR-Linac, requires choosing between different adaptation methods, namely ATP (Adapt to Position) and ATS (Adapt to Shape), where the latter requires daily re-contouring to ob...