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Using Genomics Feature Selection Method in Radiomics Pipeline Improves Prognostication Performance in Locally Advanced Esophageal Squamous Cell Carcinoma-A Pilot Study.

Chen-Yi XieYi-Huai HuJoshua Wing-Kei HoLu-Jun HanHong YangJing WenKa-On LamIan Yu-Hong WongSimon Ying-Kit LawKeith Wan Hang ChiuJian-Hua FuVarut Vardhanabhuti
Published in: Cancers (2021)
Genomics association was useful for radiomic feature selection. The established radiomic signature was prognostic for DFS. The radiomic nomogram could provide a valuable prediction for individualized long-term survival.
Keyphrases
  • locally advanced
  • machine learning
  • lymph node metastasis
  • single cell
  • deep learning
  • squamous cell carcinoma
  • rectal cancer
  • neoadjuvant chemotherapy
  • clinical trial
  • neural network
  • lymph node
  • open label
  • data analysis