AIMC Topic: Organs at Risk

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A qualitative, quantitative and dosimetric evaluation of a machine learning-based automatic segmentation method in treatment planning for gastric 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: To investigate the performance of a machine learning-based segmentation method for treatment planning of gastric cancer.

Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.

European radiology
OBJECTIVE: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at riskĀ (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.

Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT).

Physics in medicine and biology
To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated radiation therapy (IMRT) treatment plans for head-and-neck (HN) cancer with consistent organ-at-ris...

Machine learning based radiomics model to predict radiotherapy induced cardiotoxicity in breast cancer.

Journal of applied clinical medical physics
PURPOSE: Cardiotoxicity is one of the major concerns in breast cancer treatment, significantly affecting patient outcomes. To improve the likelihood of favorable outcomes for breast cancer survivors, it is essential to carefully balance the potential...

Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.

International journal of radiation oncology, biology, physics
PURPOSE: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance.

Automatic plan selection using deep network-A prostate study.

Medical physics
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...

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
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_...

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
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
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...