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Nomogram predicts the prognosis of patients with thymic carcinoma: A population-based study using SEER data.

Yang-Yu HuangXuan LiuShen-Hua LiangLei-Lei WuGuo-Wei Ma
Published in: Tumori (2022)
We developed nomograms using eight clinicopathological factors that predicted OS and CSS among TC patients. The nomograms performed better than the traditional Masaoka staging system and could identify high-risk patients. Based on the nomograms' performance, we believe they will be useful prognostication tools for TC patients.
Keyphrases
  • end stage renal disease
  • ejection fraction
  • newly diagnosed
  • machine learning
  • artificial intelligence