AIMC Topic: Organs at Risk

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Knowledge-based trade-off prediction for NSCLC treatment planning using multi-output regression.

Medical physics
BACKGROUND: Knowledge-based planning (KBP) is a data-driven approach that utilizes the knowledge from previous high-quality treatment plans to predict dose-volume histogram (DVH) parameters for organs-at-risk (OARs) in new cases. Research has demonst...

Impact of deep learning model uncertainty on manual corrections to MRI-based auto-segmentation in prostate cancer radiotherapy.

Journal of applied clinical medical physics
BACKGROUND: Deep learning (DL)-based organ segmentation is increasingly used in radiotherapy. While methods exist to generate voxel-wise uncertainty maps from DL-based auto-segmentation models, these maps are rarely presented to clinicians.

Automatic contour quality assurance using deep-learning based contours.

Physics in medicine and biology
Safe deployment of auto-contouring models requires the inclusion of automated quality assurance (QA). One such approach is to use two independent auto-contouring models and compare them geometrically for acceptability. This is not effective because g...

U-Attention-Net: a deep learning automatic delineation model for parotid glands in head and neck cancer organs at risk on radiotherapy localization computed tomography images.

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)
OBJECTIVE: This study aimed to develop a novel deep learning model, U-Attention-Net (UA-Net), for precise segmentation of parotid glands on radiotherapy localization CT images.

Deep learning-based auto-contouring of organs/structures-at-risk for pediatric upper abdominal radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSES: This study aimed to develop a computed tomography (CT)-based multi-organ segmentation model for delineating organs-at-risk (OARs) in pediatric upper abdominal tumors and evaluate its robustness across multiple datasets.

Automated field-in-field planning for tangential breast radiation therapy based on digitally reconstructed radiograph.

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)
BACKGROUND: The tangential field-in-field (FIF) technique is a widely used method in breast radiation therapy, known for its efficiency and the reduced number of fields required in treatment planning. However, it is labor-intensive, requiring manual ...

Virtual monochromatic image-based automatic segmentation strategy using deep learning method.

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)
BACKGROUND AND PURPOSE: The image quality of single-energy CT (SECT) limited the accuracy of automatic segmentation. Dual-energy CT (DECT) may potentially improve automatic segmentation yet the performance and strategy have not been investigated thor...

On factors that influence deep learning-based dose prediction of head and neck tumors.

Physics in medicine and biology
This study investigates key factors influencing deep learning-based dose prediction models for head and neck cancer radiation therapy. The goal is to evaluate model accuracy, robustness, and computational efficiency, and to identify key components ne...

Deep learning-powered radiotherapy dose prediction: clinical insights from 622 patients across multiple sites tumor at a single institution.

Radiation oncology (London, England)
PURPOSE: Accurate pre-treatment dose prediction is essential for efficient radiotherapy planning. Although deep learning models have advanced automated dose distribution, comprehensive multi-tumor analyses remain scarce. This study assesses deep lear...