A5057283050- Radiomics and Machine Learning in Medical Imaging
- Medical Imaging Techniques and Applications
- Scientific Computing and Data Management
- Neuroblastoma Research and Treatments
- Image Retrieval and Classification Techniques
- Advanced X-ray and CT Imaging
Instituto de Investigación Sanitaria La Fe
2023-2024
University of Cologne
2024
To externally validate and assess the accuracy of a previously trained fully automatic nnU-Net CNN algorithm to identify segment primary neuroblastoma tumors in MR images large children cohort.An international multicenter, multivendor imaging repository patients with neuroblastic was used performance Machine Learning (ML) tool delineate tumors. The dataset heterogeneous completely independent from one train tune model, consisting 300 having 535 T2-weighted sequences (486 at diagnosis 49...
Purpose To evaluate the reproducibility of radiomics features extracted from T2-weighted MR images in patients with neuroblastoma. Materials and Methods A retrospective study included 419 (mean age, 29 months ± 34 [SD]; 220 male, 199 female) neuroblastic tumors diagnosed between 2002 2023, within scope PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis prognosis, Empowered by imaging biomarkers (ie, PRIMAGE) project, involving 746 T2/T2*-weighted MRI sequences...