A5075187500- Advanced Radiotherapy Techniques
- Health Systems, Economic Evaluations, Quality of Life
- Head and Neck Cancer Studies
- Voice and Speech Disorders
- Statistical Methods in Clinical Trials
- Radiomics and Machine Learning in Medical Imaging
- Dysphagia Assessment and Management
- Tracheal and airway disorders
- Medical Imaging and Analysis
- Medical Imaging Techniques and Applications
- Prostate Cancer Diagnosis and Treatment
- Prostate Cancer Treatment and Research
- AI in cancer detection
- Urological Disorders and Treatments
- Spectroscopy and Chemometric Analyses
- Pharmaceutical studies and practices
- Advanced X-ray and CT Imaging
Laboratoire Traitement du Signal et de l'Image
2022-2025
Universidad Carlos III de Madrid
2022-2025
Inserm
2023-2025
Université de Rennes
2024
Centre Eugène Marquis
2022-2024
BioMarin (United States)
2024
Takeda (United States)
2024
Novartis (Switzerland)
2024
Novo Nordisk (Mexico)
2024
Bayer (United States)
2024
Abstract Objective. Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger accelerate replanning but is still a challenge due to poor soft tissue contrast, artifacts, limited field-of-view these images, alongside lack large, annotated datasets train deep learning models. This study aims develop comprehensive framework segment 25 HN OARs...
The intraprostatic urethra is an organ at risk in prostate cancer radiotherapy, but its segmentation computed tomography (CT) challenging. This work sought to: i) propose automatic pipeline for CT, ii) analyze the dose to urethra, iii) compare predictions magnetic resonance (MR) contours.First, we trained Deep Learning networks segment rectum, bladder, prostate, and seminal vesicles. Then, proposed Urethra Segmentation model was with bladder distance transforms 44 labeled CT visible...
One of the most important steps in head and neck (HN) cancer radiotherapy treatment planning is to accurately delineate organs at risk (OARs). Deep learning (DL) has proven be an efficient tool for this task, but its implementation into clinic hindered by a lack trust among users, other factors. We propose evaluate DL-based segmentation with following metrics: (a) clinical assessment analyze acceptability predicted OAR segmentations; (b) classification method identify possible erroneous...
Radiotherapy is one of the main treatments for localized head and neck (HN) cancer. To design a personalized treatment with reduced radio-induced toxicity, accurate delineation organs at risk (OAR) crucial step. Manual time- labor-consuming, as well observer-dependent. Deep learning (DL) based segmentation has proven to overcome some these limitations, but requires large databases homogeneously contoured image sets robust training. However, are not easily obtained from standard clinical...
Introduction:The increase in life expectancy among individuals with haemophilia recent years has led to a rise age-related comorbidities.Alongside the emergence of new treatments and their more frequent use various clinical scenarios such as surgeries, invasive diagnostic procedures, on-demand treatment for bleeding episodes, there is need train nursing staff from units other than haematology handling, preparation administration haemostatic treatment.Methods: An educational workshop was...
The association between dose to selected bladder and rectum symptom-related sub-regions (SRS) late toxicity after prostate cancer radiotherapy has been evidenced by voxel-wise analyses. aim of the current study was explore feasibility combining knowledge-based (KB) multi-criteria optimization (MCO) spare SRSs without compromising planning target volume (PTV) delivery, including pelvic-node irradiation.Forty-five previously treated patients (74.2 Gy/28fr) were (in bladder, associated with...