Advancing musculoskeletal tumor diagnosis: Automated segmentation and predictive classification using deep learning and radiomics.

Journal: Computers in biology and medicine
Published Date: Apr 22, 2024

Abstract

OBJECTIVES: Musculoskeletal (MSK) tumors, given their high mortality rate and heterogeneity, necessitate precise examination and diagnosis to guide clinical treatment effectively. Magnetic resonance imaging (MRI) is pivotal in detecting MSK tumors, as it offers exceptional image contrast between bone and soft tissue. This study aims to enhance the speed of detection and the diagnostic accuracy of MSK tumors through automated segmentation and grading utilizing MRI.

Authors

  • Shuo Wang
    College of Tea & Food Science, Anhui Agricultural University, Hefei, China.
  • Man Sun
    Radiology Department, Tianjin University Tianjin Hospital, Tianjin, 300299, China. Electronic address: sunman200@163.com.
  • Jinglai Sun
    Department of Biomedical Engineering, Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments, Tianjin University, Tianjin, China. sunjinglai@tju.edu.cn.
  • Qingsong Wang
    Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China. Electronic address: wqs_bme@tju.edu.cn.
  • Guangpu Wang
    Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China.
  • Xiaolin Wang
    Department of Urology, Nantong Tumor Hospital, Nantong, Jiangsu, China.
  • Xianghong Meng
    College of Food Science and Engineering, Ocean University of China, Qingdao, China.
  • Zhi Wang
    Department of Pharmacy, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • Hui Yu
    Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.

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