Anales de la RANM

220 A N A L E S R A N M R E V I S T A F U N D A D A E N 1 8 7 9 TUMORES DE ESTIRPE NEUROBLÁSTICA EN EDAD PEDIÁTRICA Martí-Bonmatí L, et al. An RANM. 2024;141(03): 209 - 220 radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multis- cale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by ima- ging biomarkers) project. Pediatr Radiol. 2023; 54(4): 562-570. 24. Ebner H, Hayn D, Falgenhauer M et al. Piloting the European Unified Patient Identity Manage- ment (EUPID): Concept to facilitate secondary use of neuroblastoma data from clinical trials and biobanking. Stud Health Technol Inf. 2016; 223: 31-38. 25. QUIBIM Precision. https://cordis.europa.eu/ project/id/778064. 26. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods. 2021; 18(2): 203-211. 27. Veiga-Canuto D, Cerdà-Alberich L, Sangüesa Nebot C et al. Comparative multicentric evalua- tion of inter-observer variability in manual and automatic segmentation of neuroblastic tumors in magnetic resonance images. Cancers. 2022; 14(15): 3648. 28. Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945; 26: 297-302. 29. Zou KH, Warfield SK, Bharatha A et al. Statis- tical validation of image segmentation quality based on a spatial overlap index1. Acad Radiol. 2004; 11(2): 178-189. 30. Van Griethuysen JJM, Fedorov A, Parmar C. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017; 77(21): e104-107. 31. Lundberg SM, Lee SI. A Unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S et al. (Eds.) Advances in neural information processing systems 30. Red Hook, NY: Curran Associates; 2017. p.4765- 4774. 32. Veiga-Canuto D, Cerdà-Alberich L, Jiménez- Pastor A et al. Independent validation of a deep learning nnu-Net tool for neuroblastoma detec- tion and segmentation in MR images. Cancers. 2023; 15(5): 1622. 33. Veiga-Canuto D, Fernández-Patón M, Alberich LC et al. Reproducibility analysis of radiomic features from t2-weighted MRI after processing and segmentation alternations in neuroblas- toma tumors. Radiol Artif Intell. 2024; 6(4): e230208. 34. Meyer CR, Johnson TD, McLennan G et al. Evaluation of lung MDCT nodule annotation across radiologists and methods. Acad Radiol. 2006; 13(10): 1254-1265. 35. Montagne S, Hamzaoui D, Allera A et al. Cha- llenge of prostate MRI segmentation on T2- weighted images: Inter-observer variability and impact of prostate morphology. Insights Ima- ging. 2021; 12(1): 71. 36. Alves N, Schuurmans M, Litjens G, Bosma JS, Hermans J, Huisman H. Fully automatic deep learning framework for pancreatic ductal ade- nocarcinoma detection on computed tomogra- phy. Cancers. 2022; 14(2): 376. 37. Granzier RWY, Verbakel NMH, Ibrahim A et al. MRI-based radiomics in breast cancer: Featu- re robustness with respect to inter-observer segmentation variability. Sci Rep. 2020; 10(1): 14163. 38. Wang H, Chen X, Yu W et al. Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglio- neuroblastoma/ganglioneuroma in children: an exploratory study. Abdom Radiol. 2023; 48(4): 1372-1382. 39. Zhou Z. Artificial intelligence on MRI for mo- lecular subtyping of diffuse gliomas: Feature comparison, visualization, and correlation bet- ween radiomics and deep learning. Eur Radiol. 2022; 32(2): 745-746. 40. Li G, Li L, Li Y et al. An MRI radiomics approach to predict survival and tumour-infiltrating ma- crophages in gliomas. Brain. 2022;145(3): 1151- 1161. Si desea citar nuestro artículo: Martí-Bonmatí L, Veiga-Canuto D, Cerdá Alberich L. Modelo pre- dictivo de la agresividad biológica de tumores de estirpe neuro- blástica en edad pediátrica incorporando la radiómica. An RANM. 2024;141(03): 209– 220. DOI: 10.32440/ar.2024.141.03. rev02