Predicting pathological highly invasive lung cancer from preoperative [

HIGHLIGHTS

• who: Yuki Onozato from the Division of Thoracic Surgery, Chiba Cancer Centre, u20112, Nitonau2011Cho, Chuou2011Ku, Chiba, u20118717, Japan have published the research work: Predicting pathological highly invasive lung cancer from preoperative [, in the Journal: (JOURNAL)
• what: This study presented calibration plots and the results of a DCA for highly invasive and less-highly invasive lung cancer.
• how: The authors developed and validated multiple machine learning models predicting pathological invasiveness of lung cancer based on preoperative u00ad[18F]fluorodeoxyglucose (FDG) positron emission tomography (PET) and computed tomography (CT) radiomic features. The original 107 features . . .
  

   

  

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