Anales de la RANM

118 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 INTELIGENCIA ARTIFICIAL EN IMAGEN MÉDICA Martí-Bonmatí L An RANM. 2024;141(02): 111 - 118 18. Veiga-Canuto D, Cerdá Alberich L, Fernández- Patón M, et al. Imaging biomarkers and radio- mics in pediatric oncology: a view from the PRI- MAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project. Pediatr Radiol. 2023. doi: 10.1007/ s00247-023-05770-y. 19. Veiga-Canuto D, Cerdà-Alberich L, Jiménez-Pastor A, et al. Independent Validation of a Deep Lear- ning nnU-Net Tool for Neuroblastoma Detection and Segmentation in MR Images. Cancers (Basel). 2023;15(5):1622. doi: 10.3390/cancers15051622. 20. Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, et al. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol. 2022;95(1137):20220072. doi: 10.1259/bjr.20220072. 21. Scapicchio C, Gabelloni M, Forte SM, et al. DI- COM-MIABIS integration model for biobanks: a use case of the EU PRIMAGE project. Eur Radiol Exp. 2021;5(1):20. doi: 10.1186/s41747- 021-00214-4. 22. Martí-Aguado D, Jiménez-Pastor A, Alberich- Bayarri Á, et al. Automated Whole-Liver MRI Segmentation to assess steatosis and iron quantification in chronic liver disease. Radio- logy. 2022;302(2):345-354. doi: 10.1148/ra- diol.2021211027. Si desea citar nuestro artículo: Martí-Bonmatí L. Inteligencia artificial en imagen médica. An RANM. 2024;141(02): 111–118. DOI: 10.32440/ar.2024.141.02. rev02