AIMC Topic: Organs at Risk

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Cluster-Based Toxicity Estimation of Osteoradionecrosis Via Unsupervised Machine Learning: Moving Beyond Single Dose-Parameter Normal Tissue Complication Probability by Using Whole Dose-Volume Histograms for Cohort Risk Stratification.

International journal of radiation oncology, biology, physics Mar 10, 2024
PURPOSE: Given the limitations of extant models for normal tissue complication probability estimation for osteoradionecrosis (ORN) of the mandible, the purpose of this study was to enrich statistical inference by exploiting structural properties of d...
Aged Adult Male Middle Aged Humans Retrospective Studies Female Probability Radiotherapy Dosage Osteoradionecrosis Support Vector Machine Mandibular Diseases Risk Assessment Cluster Analysis Mandible Unsupervised Machine Learning Head and Neck Neoplasms Organs at Risk
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A Prospective Study of Machine Learning-Assisted Radiation Therapy Planning for Patients Receiving 54 Gy to the Brain.

International journal of radiation oncology, biology, physics Mar 1, 2024
PURPOSE: The capacity for machine learning (ML) to facilitate radiation therapy (RT) planning for primary brain tumors has not been described. We evaluated ML-assisted RT planning with regard to clinical acceptability, dosimetric outcomes, and planni...
Adult Humans Brain Neoplasms Machine Learning Male Organs at Risk Prospective Studies Middle Aged Radiotherapy Planning, Computer-Assisted Female Adolescent Radiotherapy Dosage Aged Child, Preschool Dose Fractionation, Radiation Young Adult Child
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Experience of Implementing Deep Learning-Based Automatic Contouring in Breast Radiation Therapy Planning: Insights From Over 2000 Cases.

International journal of radiation oncology, biology, physics Feb 29, 2024
PURPOSE: This study evaluated the impact and clinical utility of an auto-contouring system for radiation therapy treatments.
Automation Radiotherapy, Adjuvant Humans Heart Female Breast Retrospective Studies Breast Neoplasms Organs at Risk Deep Learning Radiotherapy Planning, Computer-Assisted
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Deep learning segmentation of organs-at-risk with integration into clinical workflow for pediatric brain radiotherapy.

Journal of applied clinical medical physics Feb 19, 2024
PURPOSE: Radiation therapy (RT) of pediatric brain cancer is known to be associated with long-term neurocognitive deficits. Although target and organs-at-risk (OARs) are contoured as part of treatment planning, other structures linked to cognitive fu...
Infant Deep Learning Adolescent Child, Preschool Child Image Processing, Computer-Assisted Workflow Brain Young Adult Adult Brain Neoplasms Organs at Risk Humans Radiotherapy Planning, Computer-Assisted
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Deep learning for head and neck semi-supervised semantic segmentation.

Physics in medicine and biology Feb 19, 2024
. Radiation therapy (RT) represents a prevalent therapeutic modality for head and neck (H&N) cancer. A crucial phase in RT planning involves the precise delineation of organs-at-risks (OARs), employing computed tomography (CT) scans. Nevertheless, th...
Image Processing, Computer-Assisted Head and Neck Neoplasms Organs at Risk Semantics Humans Tomography, X-Ray Computed Deep Learning
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[Not Available].

Medical physics Feb 6, 2024
BACKGROUND:: Efficient and accurate delineation of organs at risk (OARs) is a critical procedure for treatment planning and dose evaluation. Deep learning-based auto-segmentation of OARs has shown promising results and is increasingly being used in r...
Image Processing, Computer-Assisted Radiation Oncology Organs at Risk Radiotherapy Planning, Computer-Assisted Tomography, X-Ray Computed Male Deep Learning Humans Neural Networks, Computer Artificial Intelligence
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Custom-Trained Deep Learning-Based Auto-Segmentation for Male Pelvic Iterative CBCT on C-Arm Linear Accelerators.

Practical radiation oncology Feb 6, 2024
PURPOSE: The purpose of this investigation was to evaluate the clinical applicability of a commercial artificial intelligence-driven deep learning auto-segmentation (DLAS) tool on enhanced iterative cone beam computed tomography (iCBCT) acquisitions ...
Pelvis Organs at Risk Radiotherapy Planning, Computer-Assisted Particle Accelerators Male Humans Deep Learning Cone-Beam Computed Tomography Prostatic Neoplasms
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Deep learning and atlas-based models to streamline the segmentation workflow of total marrow and lymphoid irradiation.

La Radiologia medica Feb 2, 2024
PURPOSE: To improve the workflow of total marrow and lymphoid irradiation (TMLI) by enhancing the delineation of organs at risk (OARs) and clinical target volume (CTV) using deep learning (DL) and atlas-based (AB) segmentation models.
Humans Deep Learning Workflow Bone Marrow Organs at Risk Radiotherapy Planning, Computer-Assisted Lymphatic Irradiation
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Clinical assessment of deep learning-based uncertainty maps in lung cancer segmentation.

Physics in medicine and biology Jan 24, 2024
. Prior to radiation therapy planning, accurate delineation of gross tumour volume (GTVs) and organs at risk (OARs) is crucial. In the current clinical practice, tumour delineation is performed manually by radiation oncologists, which is time-consumi...
Radiotherapy Planning, Computer-Assisted Uncertainty Image Processing, Computer-Assisted Lung Neoplasms Organs at Risk Humans Reproducibility of Results Deep Learning
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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...
Organs at Risk Algorithms Radiotherapy Planning, Computer-Assisted Radiotherapy, Intensity-Modulated Humans Tomography, X-Ray Computed Radiotherapy Dosage Artificial Intelligence Image Processing, Computer-Assisted Software Head and Neck Neoplasms
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