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Congress: ECR25
Poster Number: C-15066
Type: Poster: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2025/C-15066
Authorblock: J. Fu1, M. Fang2, Z. Wang2, D. Dong2, Z. Zheng1; 1Beijing/CN, 2Beijng/CN
Disclosures:
Jia Fu: Nothing to disclose
Mengjie Fang: Nothing to disclose
Zipei Wang: Nothing to disclose
Di Dong: Nothing to disclose
Zhuozhao Zheng: Nothing to disclose
Keywords: Abdomen, Gastrointestinal tract, Oncology, CT, Computer Applications-General, Cancer
References
  1. https://gco.iarc.who.int/media/globocan/factsheets/cancers/39-all-cancers-fact-sheet.pdf
  2. Wang FH, Zhang XT, Tang L,et al. The Chinese Society of Clinical Oncology (CSCO): Clinical guidelines for the diagnosis and treatment of gastric cancer, 2023. Cancer Commun (Lond). 2024;44(1):127-172.
  3. Sun RJ, Fang MJ, Tang L,et al. CT-based deep learning radiomics analysis for evaluation of serosa invasion in advanced gastric cancer. Eur J Radiol. 2020 ;132:109277
  4. Orlhac F, Frouin F, Nioche C, Ayache N, Buvat I. Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics. Radiology. 2019;291(1):53-59.
  5. Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, Rodriguez-Rivera E, Dodge C, Jones AK, Court L. Measuring Computed Tomography Scanner Variability of Radiomics Features. Invest Radiol. 2015;50(11):757-65.
  6. Papadimitroulas P, Brocki L, Christopher Chung N,et al. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization. Phys Med. 2021 ;83:108-121. 
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