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Organs at Risk

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Artificial intelligence-based motion tracking in cancer radiotherapy: A review.

Journal of applied clinical medical physics
Radiotherapy aims to deliver a prescribed dose to the tumor while sparing neighboring organs at risk (OARs). Increasingly complex treatment techniques such as volumetric modulated arc therapy (VMAT), stereotactic radiosurgery (SRS), stereotactic body...

FA-Net: A hierarchical feature fusion and interactive attention-based network for dose prediction in liver cancer patients.

Artificial intelligence in medicine
Dose prediction is a crucial step in automated radiotherapy planning for liver cancer. Several deep learning-based approaches for dose prediction have been proposed to enhance the design efficiency and quality of radiotherapy plan. However, these app...

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.

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer.

International journal of radiation oncology, biology, physics
PURPOSE: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value variables, which, for radiation-induced xerostomia, are baseline xerostomia and mean salivary gland ...

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...

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...

Clinical implementation and evaluation of deep learning-assisted automatic radiotherapy treatment planning for lung cancer.

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)
PURPOSE: The purpose of the study is to investigate the clinical application of deep learning (DL)-assisted automatic radiotherapy planning for lung cancer.

Deep-learning-based segmentation using individual patient data on prostate cancer radiation therapy.

PloS one
PURPOSE: Organ-at-risk segmentation is essential in adaptive radiotherapy (ART). Learning-based automatic segmentation can reduce committed labor and accelerate the ART process. In this study, an auto-segmentation model was developed by employing ind...