AIMC Topic: Radiotherapy Planning, Computer-Assisted
Clear Filters Showing 81 to 90 of 728 articles
Incorporating patient-specific prior clinical knowledge to improve clinical target volume auto-segmentation generalisability for online adaptive radiotherapy of rectal cancer: A multicenter validation.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 13, 2024
BACKGROUND & PURPOSE: Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in th...
Towards U-Net-based intraoperative 2D dose prediction in high dose rate prostate brachytherapy.
Brachytherapy
Dec 11, 2024
BACKGROUND: Poor needle placement in prostate high-dose-rate brachytherapy (HDR-BT) results in sub-optimal dosimetry and mentally predicting these effects during HDR-BT is difficult, creating a barrier to widespread availability of high-quality prost...
Automatic plan selection using deep network-A prostate study.
Medical physics
Dec 10, 2024
BACKGROUND: Recently, high-dose-rate (HDR) brachytherapy treatment plans generation was improved with the development of multicriteria optimization (MCO) algorithms that can generate thousands of pareto optimal plans within seconds. This brings a shi...
[Application of artificial intelligence-based organ contouring software and comparison of results in treatment planning for radiotherapy].
Orvosi hetilap
Dec 8, 2024
An assessment of the influence of trade-off optimization in commercial knowledge based planning library creation for tongue cancer patients.
Medical dosimetry : official journal of the American Association of Medical Dosimetrists
Dec 6, 2024
This article aims to compare the dosimetric performance between knowledge-based plan (KBP) libraries with and without trade-off (TO) exploration using multicriterial optimization (MCO) for tongue cancer patients. The trade-off optimized library (KBP_...
Federated learning for enhanced dose-volume parameter prediction with decentralized data.
Medical physics
Dec 6, 2024
BACKGROUND: The widespread adoption of knowledge-based planning in radiation oncology clinics is hindered by the lack of data and the difficulty associated with sharing medical data.
Deep learning dose prediction to approach Erasmus-iCycle dosimetric plan quality within seconds for instantaneous treatment planning.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Dec 6, 2024
BACKGROUND AND PURPOSE: Fast, high-quality deep learning (DL) prediction of patient-specific 3D dose distributions can enable instantaneous treatment planning (IP), in which the treating physician can evaluate the dose and approve the plan immediatel...
Estimation of heart dose in left breast cancer radiotherapy: Assessment of vDIBH feasibility using the supervised machine learning algorithm.
Journal of applied clinical medical physics
Dec 6, 2024
BACKGROUND AND OBJECTIVE: The volunteer deep inspiration breath hold (vDIBH) technique is used to reduce the heart dose in left breast cancer radiotherapy. Many times, it is faced that despite rigorous exercise and training, not all patients get bene...
Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.
Journal of applied clinical medical physics
Dec 3, 2024
PURPOSE: Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment p...
Breast radiotherapy planning: A decision-making framework using deep learning.
Medical physics
Dec 3, 2024
BACKGROUND: Effective breast cancer treatment planning requires balancing tumor control while minimizing radiation exposure to healthy tissues. Choosing between intensity-modulated radiation therapy (IMRT) and three-dimensional conformal radiation th...
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