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Robustness and performance of radiomic features in diagnosing cystic renal masses.

Arda KönikNityanand MiskinYang GuoAtul B ShinagareLei Qin
Published in: Abdominal radiology (New York) (2021)
AUC ranged for the robust and uncorrelated features from 0.83 ± 0.09 to 0.93 ± 0.04 and for the first-order features from 0.84 ± 0.09 to 0.91 ± 0.04. Our study indicates that the first-order features alone are sufficient for the classification of CRMs, and that inclusion of higher-order features does not necessarily improve performance.
Keyphrases
  • machine learning
  • deep learning
  • computed tomography
  • magnetic resonance