Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

Journal: BioMed research international
Published Date: Mar 11, 2021

Abstract

OBJECTIVES: To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors.

Authors

  • Derun Pan
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
  • Renyi Liu
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
  • Bowen Zheng
    Department of Mechanical Engineering, University of California, Berkeley, CA, 94720, USA.
  • Jianxiang Yuan
    Department of Radiology, Foshan Hospital of TCM, Foshan, Guangdong Province, China.
  • Hui Zeng
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Zilong He
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Zhendong Luo
    Department of Radiology, University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong Province, China.
  • Genggeng Qin
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Weiguo Chen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: chenweiguo1964@21cn.com.

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