Machine learning algorithms integrating positron emission tomography/computed tomography features to predict pathological complete response after neoadjuvant chemoimmunotherapy in lung cancer.

Journal: European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery
PMID: 40221851

Abstract

OBJECTIVES: Reliable methods for predicting pathological complete response (pCR) in non-small cell lung cancer (NSCLC) patients undergoing neoadjuvant chemoimmunotherapy are still under exploration. Although Fluorine-18 fluorodeoxyglucose-positron emission tomography/computed tomography (18F-FDG PET/CT) features reflect tumour response, their utility in predicting pCR remains controversial.

Authors

  • Zhenxin Sheng
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Shuyu Ji
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Yancheng Chen
    Alibaba Group.
  • Zirong Mi
    Department of Thoracic Surgery, Medical College of Shihezi University, Shihezi, China.
  • Huansha Yu
    Experimental Animal Center, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Lele Zhang
  • Shiyue Wan
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
  • Nan Song
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
  • Ziyun Shen
    National Engineering Research Center for Cereal Fermentation and Food Biomanufacturing, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122, People's Republic of China.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.

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