Machine learning for differentiation of lipid-poor adrenal adenoma and subclinical pheochromocytoma based on multiphase CT imaging radiomics

被引：0
|
作者：
Dao-xiong Xiao
Jian-ping Zhong
Ji-dong Peng
Cun-geng Fan
Xiao-chun Wang
Xing-lin Wen
Wei-wei Liao
Jun Wang
Xiao-feng Yin
机构：
[1] Ganzhou Hospital affiliated to Nanchang University,Department of Medical Imaging
[2] Ganzhou People’s Hospital,Department of Medical Imaging
[3] the First Affiliated Hospital of Gannan Medical University,Department of Medical Imaging
[4] Nankang District People’s Hospital,undefined
来源：
BMC Medical Imaging | / 23卷
关键词：
Computed tomography; Machine learning; Radiomics; Lipid-poor adrenal adenoma; Subclinical pheochromocytoma;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
引用
收藏
相关论文
共 50 条
  • [31] A modified method for CT radiomics region-of-interest segmentation in adrenal lipid-poor adenomas: a two-institution comparative study
    Zhu, Hanlin
    Wu, Mengwei
    Wei, Peiying
    Tian, Min
    Zhang, Tong
    Hu, Chunfeng
    Han, Zhijiang
    FRONTIERS IN ONCOLOGY, 2023, 13
  • [32] Using CT radiomic features based on machine learning models to subtype adrenal adenoma
    Shouliang Qi
    Yifan Zuo
    Runsheng Chang
    Kun Huang
    Jing Liu
    Zhe Zhang
    BMC Cancer, 23
  • [33] Using CT radiomic features based on machine learning models to subtype adrenal adenoma
    Qi, Shouliang
    Zuo, Yifan
    Chang, Runsheng
    Huang, Kun
    Liu, Jing
    Zhang, Zhe
    BMC CANCER, 2023, 23 (01)
  • [34] Differentiating between adrenocortical carcinoma and lipid-poor cortical adenoma: A novel cross-sectional imaging-based score
    Yalon, Tal
    Yalon, Mariana
    Assaf, Dan
    Lenartowicz, Karina
    Foster, Trenton
    Lyden, Melanie
    Dy, Benzon
    Bancos, Irina
    McKenzie, Travis
    SURGERY, 2023, 173 (01) : 35 - 42
  • [35] Development and validation of a model predicting adrenal lipid-poor adenoma based on the minimum attenuation value from non-contrast CT: a dual-center retrospective study
    Zhu, Hanlin
    Wu, Mengwei
    Feng, Bo
    Zhang, Haifeng
    Hu, Chunfeng
    Zhang, Tong
    Han, Zhijiang
    BMC MEDICAL IMAGING, 2024, 24 (01):
  • [36] Differentiating between adrenocortical carcinoma and lipid-poor cortical adenoma: A novel cross-sectional imaging-based score Discussion
    Miller, Barbra
    Yalon, Tal
    Duh, Quan
    Yal, Tal
    Patel, Dhaval
    SURGERY, 2023, 173 (01) : 42 - 42
  • [37] Deep learning based classification of solid lipid-poor contrast enhancing renal masses using contrast enhanced CT
    Oberai, Assad
    Varghese, Bino
    Cen, Steven
    Angelini, Tomas
    Hwang, Darryl
    Gill, Inderbir
    Aron, Manju
    Lau, Christopher
    Duddalwar, Vinay
    BRITISH JOURNAL OF RADIOLOGY, 2020, 93 (1111):
  • [38] Machine learning-based texture analysis for differentiation of large adrenal cortical tumours on CT
    Elmohr, M. M.
    Fuentes, D.
    Habra, M. A.
    Bhosale, P. R.
    Qayyum, A. A.
    Gates, E.
    Morshid, A., I
    Hazle, J. D.
    Elsayes, K. M.
    CLINICAL RADIOLOGY, 2019, 74 (10) : 818.e1 - 818.e7
  • [39] CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study
    Zhou, Yongjie
    Zhan, Yuan
    Zhao, Jinhong
    Zhong, Linhua
    Tan, Yongming
    Zeng, Wei
    Zeng, Qiao
    Gong, Mingxian
    Li, Aihua
    Gong, Lianggeng
    Liu, Lan
    ACADEMIC RADIOLOGY, 2024, 31 (07) : 2859 - 2871
  • [40] A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging
    Peng, AiJun
    Dai, HuMing
    Duan, HaiHan
    Chen, YaXing
    Huang, JianHan
    Zhou, LiangXue
    Chen, LiangYin
    EUROPEAN JOURNAL OF RADIOLOGY, 2020, 125
12345