Anales de la RANM

29 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 Martí-Bonmatí L An RANM. 2022;139(01): 23 - 30 CÁNCER DE PÁNCREAS, RADIÓMICA E INTELIGENCIA ARTIFICIAL 18. Larue RTHM, Defraene G, De Ruysscher D, Lambin P, van Elmpt W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br J Radiol. 2017;90(1070):20160665. doi:10.1259/ bjr.20160665 19. Tomaszewski MR, Gillies RJ. The Biologi- cal Meaning of Radiomic Features. Radio- logy. 2021;298(3):505-516. doi:10.1148/ra- diol.2021202553 20. Alberich-Bayarri Á, Hernández-Navarro R, Ruiz-Martínez E, García-Castro F, García-Juan D, Martí-Bonmatí L. Development of imaging biomarkers and generation of big data. Ra- diol Med. 2017;122(6):444-448. doi:10.1007/ s11547-017-0742-x 21. Shur JD, Doran SJ, Kumar S, et al. Radiomics in Oncology: A Practical Guide. RadioGra- phics. 2021;41(6):1717-1732. doi:10.1148/ rg.2021210037 22. Mali SA, Ibrahim A, Woodruff HC, et al. Ma- king Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods. J Pers Med. 2021;11(9):842. doi:10.3390/jpm11090842 23. Fernández Patón M, Cerdá Alberich L, San- güesa Nebot C, et al. MR Denoising Increases Radiomic Biomarker Precision and Reprodu- cibility in Oncologic Imaging. J Digit Imaging. 2021;34(5):1134-1145. doi:10.1007/s10278- 021-00512-8 24. Fetty L, Bylund M, Kuess P, et al. Latent spa- ce manipulation for high-resolution medi- cal image synthesis via the StyleGAN. Z Med Phys. 2020;30(4):305-314. doi:10.1016/j.zeme- di.2020.05.001 25. Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configu- ring method for deep learning-based bio- medical image segmentation. Nat Methods. 2021;18(2):203-211. doi:10.1038/s41592-020- 01008-z 26. Meyer A, Mehrtash A, Rak M, et al. Domain adaptation for segmentation of critical struc- tures for prostate cancer therapy. 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Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma. Frontiers in Artificial Intelligence. 2020;3:77. doi:10.3389/frai.2020.550890 32. Tomaszewski MR, Latifi K, Boyer E, et al. Del- ta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer. Radiation Oncology. 2021;16(1):237. doi:10.1186/s13014-021-01957-5 33. Bian Y, Jiang H, Ma C, et al. Performance of CT- based radiomics in diagnosis of superior mesenter- ic vein resection margin in patients with pancreatic head cancer. Abdom Radiol. 2020;45(3):759-773. doi:10.1007/s00261-019-02401-9 34. Rigiroli F, Hoye J, Lerebours R, et al. CT Radiomic Features of Superior Mesenteric Artery Involve- ment in Pancreatic Ductal Adenocarcinoma: A Pilot Study. Radiology. 2021;301(3):610-622. doi:10.1148/radiol.2021210699 35. Kulkarni A, Carrion-Martinez I, Jiang NN, et al. Hypovascular pancreas head adenocarci- noma: CT texture analysis for assessment of resection margin status and high-risk features. Eur Radiol. 2020;30(5):2853-2860. doi:10.1007/ s00330-019-06583-0 36. Bian Y, Guo S, Jiang H, et al. Relationship Be- tween Radiomics and Risk of Lymph Node Me- tastasis in Pancreatic Ductal Adenocarcinoma. Pancreas. 2019;48(9):1195-1203. doi:10.1097/ MPA.0000000000001404 37. Li, K., Yao, Q., Xiao, J. et al. Contrast-enhanced CT radiomics for predicting lymph node metas- tasis in pancreatic ductal adenocarcinoma: a pi- lot study. Cancer Imaging 20, 12 (2020). https:// doi.org/10.1186/s40644-020-0288-3 38. Liang X, Cai W, Liu X, Jin M, Ruan L, Yan S. A radiomics model that predicts lymph node sta- tus in pancreatic cancer to guide clinical deci- sion making: A retrospective study. J Cancer. 2021;12(20):6050-6057. doi:10.7150/jca.61101 39. Cheng S-H, Cheng Y-J, Jin Z-Y, Xue H-D. Un- resectable pancreatic ductal adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy. European Journal of Ra- diology. 2019;113:188-197. doi:10.1016/j. ejrad.2019.02.009 40. Kim HS, Kim YJ, Kim KG, Park JS. Preopera- tive CT texture features predict prognosis af- ter curative resection in pancreatic cancer. Sci Rep. 2019;9(1):17389. doi:10.1038/s41598-019- 53831-w 41. Chang N, Cui L, Luo Y, Chang Z, Yu B, Liu Z. Development and multicenter validation of a CT-based radiomics signature for discriminat- ing histological grades of pancreatic ductal ad- enocarcinoma. Quantitative Imaging in Medi- cine and Surgery. 2020;10(3):69202-69702. doi:10.21037/qims.2020.02.21 42. Li K, Xiao J, Yang J, et al. Association of ra- diomic imaging features and gene expres- sion profile as prognostic factors in pancre- atic ductal adenocarcinoma. Am J Transl Res. 2019;11(7):4491-4499.