Development of a machine learning model for predicting pneumothorax risk in coaxial core needle biopsy (≤3 cm).

Journal: European journal of radiology
PMID: 38759543

Abstract

PURPOSE: The aim is to devise a machine learning algorithm exploiting preoperative clinical data to forecast the hazard of pneumothorax post-coaxial needle lung biopsy (CCNB), thereby informing clinical decision-making and enhancing perioperative care.

Authors

  • Xugong Zou
    Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China.
  • Ning Cui
    Department of Gastroenterology, Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, and Key Laboratory of Hubei Province for Digestive System Disease, Renmin Hospital of Wuhan University, Wuhan, China.
  • Qiang Ma
    Department of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China.
  • Zhipeng Lin
    Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China.
  • Jian Zhang
    College of Pharmacy, Ningxia Medical University, Yinchuan, NingxiaHui Autonomous Region, China.
  • Xiaoqun Li
    Department of Interventional Medicine, Zhongshan People's Hospital, Zhongshan City 528403, Guangdong Province, China. Electronic address: 413607891zxg@163.com.

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