Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor.

Journal: Journal of magnetic resonance imaging : JMRI
PMID:

Abstract

BACKGROUND: Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear.

Authors

  • Xiaohong Liang
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Kaiqiang Tang
    Department of Orthopedics, The Third Affiliated Hospital of Beijing University of Chinese Medicine, Beijing, China.
  • Xiaoai Ke
    Department of Radiology, Lanzhou University Second Hospital, Lanzhou, China.
  • Jian Jiang
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Shenglin Li
    College of Artificial Intelligence, Southwest University, Chongqing 400715, China.
  • Caiqiang Xue
    Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China. Electronic address: 1102599617@qq.com.
  • Juan Deng
    Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China. Electronic address: 2377052591@qq.com.
  • Xianwang Liu
    Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou, 730030, China; Second clinical school, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China. Electronic address: 1553537867@qq.com.
  • Cheng Yan
    Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
  • Mingzi Gao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
  • Junlin Zhou
    Department of Radiology, Lanzhou University Second Hospital, 730030 Lanzhou, Gansu, China.
  • Liqin Zhao
    Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.