Anales de la RANM

166 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 MEDICINA DE PRECISIÓN E IMAGEN MÉDICA Martí-Bonmatí L, et al. An RANM. 2026;143(02): 160 - 166 convergence of human and artificial intelli- gence. Nat Med . 2019;25(1):44-56. 5. European Society of Radiology (ESR). Medi- cal imaging in personalised medicine: a white paper of the research committee of the Euro- pean Society of Radiology. Insights Imaging . 2015;6(2):141-155. 6. Greenhalgh T, “Uncertainty and Clinical Meth- od,” in Clinical Uncertainty in Primary Care , ed. Sommers L and Launer J (Springer) 2013, 23–45. 7. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology . 2016;278(2):563-577. 8. Keenan KE, Ainslie M, Barker AJ, et al. Quanti- tative magnetic resonance imaging phantoms: A review and the need for a system phantom. Magn Reson Med . 2018;79(1):48-61. 9. Hu HH, Börnert P, Hernando D, et al. ISMRM workshop on fat-water separation: insights, applications and progress in MRI. Magn Reson Med . 2012;68(2):378-388. 10. Rinella ME, Lazarus JV, Ratziu V et al. A mul- tisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78(6):1966-1986. 11. Ratziu V, Charlotte F, Heurtier A, et al. Sam- pling variability of liver biopsy in nonalco- holic fatty liver disease. Gastroenterology . 2005;128(7):1898-1906. 12. Martí-Aguado D, Martí-Bonmatí L. Depósi- tos de hierro en la hepatopatía alcohólica y metabólica. An RANM . 2023;140(03):277–283. 13. Chen VL, Morgan TR, Rotman Y, et al. Res- metirom therapy for metabolic dysfunction- associated steatotic liver disease: October 2024 updates to AASLD Practice Guidance. Hepa- tology. 2025;81(1):312-320.. 14. Wu S, Pan J, Song M, et al. Performance of Magnetic Resonance Imaging and Ultrasound for Identifying the Different Degrees of He- patic Steatosis: A Systematic Review and Meta- analysis. Acad Radiol . 2025;32(11):6528-6540. 15. Lee DH. Attenuation Based Quantitative Eval- uation of Hepatic Steatosis Using US: Current Status and Future Perspective. J Korean Soc Ra- diol . 2025;86(3):321-334. 16. Pickhardt PJ, Lubner MG. Noninvasive Quan- titative CT for Diffuse Liver Diseases: Steato- sis, Iron Overload, and Fibrosis. Radiograph- ics . 2025;45(1):e240176. 17. Catania R, Jia L, Haghshomar M, Miller FH, Borhani AA. Detection of moderate hepatic ste- atosis on contrast-enhanced dual-source dual- energy CT: Role and accuracy of virtual non- contrast CT. Eur J Radiol . 2024;172:111328. 18. Reeder SB, Cruite I, Hamilton G, Sirlin CB. Quantitative Assessment of Liver Fat with Magnetic Resonance Imaging and Spectrosco- py. J Magn Reson Imaging . 2011;34(4):729-749. 19. Azizi N, Naghibi H, Shakiba M, et al. Evalua- tion of MRI proton density fat fraction in he- patic steatosis: a systematic review and meta- analysis. Eur Radiol . 2025;35(4):1794-1807. 20. Reeder SB, Yokoo T, França M, et al. Quan- tification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR. Radiology . 2023;307(1):e221856. 21. França M, Alberich-Bayarri A, Martí-Bonmatí L, et al. Accurate simultaneous quantification of liver steatosis and iron overload in dif- fuse liver diseases with MRI. Abdom Radiol . 2017;42(5):1434-1443. 22. Zhao R, Hamilton G, Brittain JH, Reeder SB, Hernando D. Design and evaluation of quanti- tative MRI phantoms to mimic the simultane- ous presence of fat, iron, and fibrosis in the liver. Magn Reson Med . 2021;85(2):734-747. 23. Hernando D, Zhao R, Yuan Q, et al. Multi- center Reproducibility of Liver Iron Quanti- fication with 1.5-T and 3.0-T MRI. Radiology . 2023;306(2):e213256. 24. 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. Ra- diology . 2022;302(2):345-354. 25. Jimenez-Pastor A, Marti-Aguado D, Pereira B, et al. Impact of hepatic vessels on whole liver proton density fat fraction and R2* quantifica- tion. Eur Radiol Exp . 2026;10(1):1. 26. European Association for the Study of the Liv- er. EASL Clinical Practice Guidelines on non- invasive tests for evaluation of liver disease se- verity and prognosis - 2021 update. J Hepatol . 2021;75(3):659-689. 27. Marti-Aguado D, Alfaro-Cervello C, Fernán- dez-Patón M, et al. Digital pathology and lipid droplet size as a key determinant of dis- crepancies between histology and MRI grad- ings in steatotic liver disease. Eur Radiol . 2026;36(2):1050-1060. 28. Tamada D, van der Heijden RA, Weaver J, Hernando D, Reeder SB. Confidence maps for reliable estimation of proton density fat frac- tion and R2* in the liver. Magn Reson Med . 2024;91(5):2172-2187. 29. Meneses JP, Tejos C, Makalic E, Uribe S. Non-iterative and uncertainty-aware MRI- based liver fat estimation using an unsuper- vised deep learning method. Med Image Anal . 2026;107(Pt A):103811. Si desea citar nuestro artículo: Martí-Bonmatí L, Martí-Aguado D. Incertidumbres en la Medi- cina de Precisión y los esfuerzos de la Imagen Médica para mi- nimizarlas. An RANM. 2026;143(02): 160– 166. DOI: 10.32440/ ar.2026.143.02.r ev03