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Congress: ECR25
Poster Number: C-25055
Type: Poster: EPOS Radiologist (educational)
Authorblock: G. Ambrogi, S. Perotti, L. Bottino, G. Recchia, M. Massaro, L. Bosi, M. T. Petrucci, C. Catalano; Roma/IT
Disclosures:
Giovanni Ambrogi: Nothing to disclose
Stefano Perotti: Nothing to disclose
Lorenza Bottino: Nothing to disclose
Greta Recchia: Nothing to disclose
Michele Massaro: Nothing to disclose
Leonardo Bosi: Nothing to disclose
Maria Teresa Petrucci: Nothing to disclose
Carlo Catalano: Nothing to disclose
Keywords: Artificial Intelligence, Musculoskeletal bone, Oncology, CT, Imaging sequences, Cancer
References

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[3] Ha, J.; Park, T.; Kim, H.-K.; Shin, Y.; Ko, Y.; Kim, D.W.; Sung, Y.S.; Lee, J.; Ham, S.J.; Khang, S.; et al. Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography. Sci. Rep. 2021, 11, 21656.

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[5] Fearon, K.; Strasser, F.; Anker, S.D.; Bosaeus, I.; Bruera, E.; Fainsinger, R.L.; Jatoi, A.; Loprinzi, C.; MacDonald, N.; Mantovani, G.; et al. Definition and classification of cancer cachexia: An international consensus. Lancet Oncol. 2011, 12, 489–495. 

[6] Bhinder, B.; Gilvary, C.; Madhukar, N.S.; Elemento, O. Artificial Intelligence in Cancer Research and Precision Medicine. Cancer Discov. 2021, 11, 900–915. 
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