Publications by authors named "F Tixier"
Article Synopsis
- Radiomics is a technique used in oncology that extracts quantitative information from medical images to aid in diagnosing, predicting outcomes, and assessing treatment responses for tumors, but it faces challenges in identifying abnormalities in conditions like tuberous sclerosis complex (TSC).
- The study aimed to create a radiomic workflow to pinpoint abnormal brain regions in TSC patients using a whole-brain atlas approach and generated heatmaps indicating deviations from normal brain signal patterns.
- Researchers conducted a retrospective study utilizing high-resolution MRI data from TSC patients and normal controls, identifying and comparing features of affected and unaffected brain regions to develop a model for detecting abnormalities in TSC.
Purpose: The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening.
Materials And Methods: Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network.
Filters are commonly used to enhance specific structures and patterns in images, such as vessels or peritumoral regions, to enable clinical insights beyond the visible image using radiomics. However, their lack of standardization restricts reproducibility and clinical translation of radiomics decision support tools. In this special report, teams of researchers who developed radiomics software participated in a three-phase study (September 2020 to December 2022) to establish a standardized set of filters.
View Article and Find Full Text PDFPurpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma.
Methods And Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression.
Purpose: The purpose of this guideline is to provide comprehensive information on best practices for robust radiomics analyses for both hand-crafted and deep learning-based approaches.
Methods: In a cooperative effort between the EANM and SNMMI, we agreed upon current best practices and recommendations for relevant aspects of radiomics analyses, including study design, quality assurance, data collection, impact of acquisition and reconstruction, detection and segmentation, feature standardization and implementation, as well as appropriate modelling schemes, model evaluation, and interpretation. We also offer an outlook for future perspectives.