A Novel Two-step Classification Approach for Differentiating Bone Metastases From Benign Bone Lesions in SPECT/CT Imaging.

Journal: Academic radiology
Published Date: Jul 2, 2025

Abstract

RATIONALE AND OBJECTIVES: This study aims to develop and validate a novel two-step deep learning framework for the automated detection, segmentation, and classification of bone metastases in SPECT/CT imaging, accurately distinguishing malignant from benign lesions to improve early diagnosis and facilitate personalized treatment planning.

Authors

  • Weiming Xie
    Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
  • Xueting Wang
    Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China.
  • Miao Liu
    The Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing 100853, China.
  • Lang Mai
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China (W.X., L.M., X.P., Y.Z., Z.Y.); Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China (W.X., X.W., M.L., L.M., X.P., Y.Z., J.Z., X.W., Y.D., Y.P., G.Z., Z.Y., Z.W.).
  • Haonan Shangguan
    School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning 110167, China (H.S.).
  • Xince Pan
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China (W.X., L.M., X.P., Y.Z., Z.Y.); Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China (W.X., X.W., M.L., L.M., X.P., Y.Z., J.Z., X.W., Y.D., Y.P., G.Z., Z.Y., Z.W.).
  • Ying Zhan
    Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
  • Jinxin Zhang
    Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou, China.
  • Xiaodan Wu
    Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China.
  • Yingxin Dai
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
  • Yusong Pei
    College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China; Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China.
  • Guoxu Zhang
    Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning 110016, China; College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning 110167, China. Electronic address: zhangguoxu_502@163.com.
  • Zhaomin Yao
    BioKnow Health Informatics Lab, College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, Jilin, China.
  • Zhiguo Wang
    Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, 110169, Liaoning, China.

Keywords

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