AI Medical Compendium Journal:
Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique

Showing 1 to 10 of 20 articles

Artificial intelligence and radiotherapy: Evolution or revolution?

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
The integration of artificial intelligence, particularly deep learning algorithms, into radiotherapy represents a transformative shift in the field, enhancing accuracy, efficiency, and personalized care. This paper explores the multifaceted impact of...

Differential plasma cytokine variation following X-ray or proton brain irradiation using machine-learning approaches.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: X-ray and proton irradiation have been reported to induce distinct modifications in cytokine expression in vitro and in vivo, suggesting a dissimilar inflammatory response between X-rays and protons. We aimed to investigate the differences i...

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.

Deep learning applied to dose prediction in external radiation therapy: A narrative review.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Over the last decades, the use of artificial intelligence, machine learning and deep learning in medical fields has skyrocketed. Well known for their results in segmentation, motion management and posttreatment outcome tasks, investigations of machin...

Evaluating ChatGPT to test its robustness as an interactive information database of radiation oncology and to assess its responses to common queries from radiotherapy patients: A single institution investigation.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: Commercial vendors have created artificial intelligence (AI) tools for use in all aspects of life and medicine, including radiation oncology. AI innovations will likely disrupt workflows in the field of radiation oncology. However, limited d...

Artificial intelligence solution to accelerate the acquisition of MRI images: Impact on the therapeutic care in oncology in radiology and radiotherapy departments.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: MRI is essential in the management of brain tumours. However, long waiting times reduce patient accessibility. Reducing acquisition time could improve access but at the cost of spatial resolution and diagnostic quality. A commercially availa...

Application of deep learning in radiation therapy for cancer.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
In recent years, with the development of artificial intelligence, deep learning has been gradually applied to clinical treatment and research. It has also found its way into the applications in radiotherapy, a crucial method for cancer treatment. Thi...

An integrated model combined intra- and peritumoral regions for predicting chemoradiation response of non small cell lung cancers based on radiomics and deep learning.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: The purpose of this study was to develop a model for predicting chemoradiation response in non-small cell lung cancer (NSCLC) patients by integrating radiomics and deep-learning features and combined intra- and peritumoral regions with pre-t...

Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: This study aims to perform a comprehensive systematic review of deep learning (DL) models in predicting RT-induced toxicity.

Automatic segmentation of kidneys in computed tomography images using U-Net.

Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
PURPOSE: Accurate segmentation of target volumes and organs at risk from computed tomography (CT) images is essential for treatment planning in radiation therapy. The segmentation task is often done manually making it time-consuming. Besides, it is b...