Page 46 - ARNM-3-2

Advances in Radiotherapy
& Nuclear Medicine Radiomics for gastric cancer

82. Dong D, Fang MJ, Tang L, et al. Deep learning radiomic doi: 10.1186/s12957-018-1490-7
nomogram can predict the number of lymph node metastasis 86. Marcus C, Subramaniam RM. PET/computed tomography
in locally advanced gastric cancer: An international and precision medicine: Gastric cancer. PET Clin.
multicenter study. Ann Oncol. 2020;31(7):912-920. 2017;12(4):437-447.
doi: 10.1016/j.annonc.2020.04.003 doi: 10.1016/j.cpet.2017.05.004
83. Berlth F, Bollschweiler E, Drebber U, Hoelscher AH, 87. Kim GH. Systematic endoscopic approach to early gastric
Moenig S. Pathohistological classification systems in gastric cancer in clinical practice. Gut Liver. 2021;15(6):811-817.
cancer: Diagnostic relevance and prognostic value. World J doi: 10.5009/gnl20318
Gastroenterol. 2014;20(19):5679-5684.
88. Berenguer R, Pastor-Juan MDR, Canales-Vázquez J,
doi: 10.3748/wjg.v20.i19.5679 et al. Radiomics of CT features may be nonreproducible
84. Cann C, Ciombor KK. Systemic therapy for gastric and redundant: Influence of CT acquisition parameters.
cancer: Perioperative strategies and beyond. J Surg Oncol. Radiology. 2018;288(2):407-415.
2022;125(7):1151-1160. doi: 10.1148/radiol.2018172361
doi: 10.1002/jso.26834 89. Wei L, Osman S, Hatt M, El Naqa I. Machine learning

85. Zhang N, Fei Q, Gu J, Yin L, He X. Progress of preoperative for radiomics-based multimodality and multiparametric
and postoperative radiotherapy in gastric cancer. World J modeling. Q J Nucl Med Mol Imaging. 2019;63(4):323-338.
Surg Oncol. 2018;16(1):187. doi: 10.23736/S1824-4785.19.03213-8

Volume 3 Issue 2 (2025) 38 doi: 10.36922/arnm.8350