Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods

被引:0
|
作者
Xinhui Wang
Qi Wan
Houjin Chen
Yanfeng Li
Xinchun Li
机构
[1] Beijing Jiaotong University,School of Electronic and Information Engineering
[2] The First Affiliated Hospital of Guangzhou Medical University,Department of Radiology
来源
European Radiology | 2020年 / 30卷
关键词
Magnetic resonance imaging; Lung cancer; Radiomics; Machine learning;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:4595 / 4605
页数:10
相关论文
共 50 条
  • [31] Machine learning-based multiparametric MRI radiomics for predicting poor responders after neoadjuvant chemoradiotherapy in rectal Cancer patients
    Wang, Jia
    Chen, Jingjing
    Zhou, Ruizhi
    Gao, Yuanxiang
    Li, Jie
    BMC CANCER, 2022, 22 (01)
  • [32] MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study
    Renato Cuocolo
    Arnaldo Stanzione
    Riccardo Faletti
    Marco Gatti
    Giorgio Calleris
    Alberto Fornari
    Francesco Gentile
    Aurelio Motta
    Serena Dell’Aversana
    Massimiliano Creta
    Nicola Longo
    Paolo Gontero
    Stefano Cirillo
    Paolo Fonio
    Massimo Imbriaco
    European Radiology, 2021, 31 : 7575 - 7583
  • [33] Classification of Prostate Cancer Gleason Scores Through Machine Learning From Multiparametric MRI
    Fehr, D.
    Wibmer, A.
    Gondo, T.
    Matsumoto, K.
    Vargas, H.
    Sala, E.
    Hricak, H.
    Deasy, J.
    Veeraraghavan, H.
    MEDICAL PHYSICS, 2015, 42 (06) : 3586 - 3586
  • [34] MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study
    Cuocolo, Renato
    Stanzione, Arnaldo
    Faletti, Riccardo
    Gatti, Marco
    Calleris, Giorgio
    Fornari, Alberto
    Gentile, Francesco
    Motta, Aurelio
    Dell'Aversana, Serena
    Creta, Massimiliano
    Longo, Nicola
    Gontero, Paolo
    Cirillo, Stefano
    Fonio, Paolo
    Imbriaco, Massimo
    EUROPEAN RADIOLOGY, 2021, 31 (10) : 7575 - 7583
  • [35] Machine and deep learning methods for radiomics
    Avanzo, Michele
    Wei, Lise
    Stancanello, Joseph
    Vallieres, Martin
    Rao, Arvind
    Morin, Olivier
    Mattonen, Sarah A.
    El Naqa, Issam
    MEDICAL PHYSICS, 2020, 47 (05) : E185 - E202
  • [36] Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models
    Sarv Priya
    Tanya Aggarwal
    Caitlin Ward
    Girish Bathla
    Mathews Jacob
    Alicia Gerke
    Eric A. Hoffman
    Prashant Nagpal
    Scientific Reports, 11
  • [37] Radiomics side experiments and DAFIT approach in identifying pulmonary hypertension using Cardiac MRI derived radiomics based machine learning models
    Priya, Sarv
    Aggarwal, Tanya
    Ward, Caitlin
    Bathla, Girish
    Jacob, Mathews
    Gerke, Alicia
    Hoffman, Eric A.
    Nagpal, Prashant
    SCIENTIFIC REPORTS, 2021, 11 (01)
  • [38] Prediction of High-Risk Cytogenetic Status in Multiple Myeloma Based on Magnetic Resonance Imaging: Utility of Radiomics and Comparison of Machine Learning Methods
    Liu, Jianfang
    Zeng, Piaoe
    Guo, Wei
    Wang, Chunjie
    Geng, Yayuan
    Lang, Ning
    Yuan, Huishu
    JOURNAL OF MAGNETIC RESONANCE IMAGING, 2021, 54 (04) : 1303 - 1311
  • [39] Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme
    Zhang, Di
    Luan, Jixin
    Liu, Bing
    Yang, Aocai
    Lv, Kuan
    Hu, Pianpian
    Han, Xiaowei
    Yu, Hongwei
    Shmuel, Amir
    Ma, Guolin
    Zhang, Chuanchen
    FRONTIERS IN MEDICINE, 2023, 10
  • [40] Comparison of Feature Selection Methods and Machine Learning Classifiers with CT Radiomics-Based Features for Predicting Chronic Obstructive Pulmonary Disease
    Makimoto, K.
    Au, R. C.
    Moslemi, A.
    Hogg, J. C.
    Bourbeau, J.
    Tan, W. C.
    Kirby, M.
    AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, 2022, 205