Differentiating Benign from Malignant Cystic Renal Masses using CT Texture-based Machine Learning Algorithms

被引：1
|
作者：
Ranlachandran, Anupama
机构：
来源：
RADIOLOGY-IMAGING CANCER | 2024年 / 6卷 / 02期
关键词：
D O I：
10.1148/rycan.249007
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
[No abstract available]
引用
收藏
页数：1
相关论文
共 50 条
  • [41] A Deep Learning Based Integration of Multiple Texture Patterns from Intensity, Gradient and Curvature GLCMs in Differentiating the Malignant from Benign Polyps
    Zhang, Shu
    Cao, Weiguo
    Pomeroy, Marc
    Gao, Yongfeng
    Tan, Jiaxing
    Liang, Zhengrong
    MEDICAL IMAGING 2020: COMPUTER-AIDED DIAGNOSIS, 2020, 11314
  • [42] Evaluation of Combined Cancer Markers With Lactate Dehydrogenase and Application of Machine Learning Algorithms for Differentiating Benign Disease From Malignant Ovarian Cancer
    Jeong, Seri
    Son, Dae-Soon
    Cho, Minseob
    Lee, Nuri
    Song, Wonkeun
    Shin, Saeam
    Park, Sung-Ho
    Lee, Dong Jin
    Park, Min-Jeong
    CANCER CONTROL, 2021, 28
  • [43] Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia
    Damiano Caruso
    Francesco Pucciarelli
    Marta Zerunian
    Balaji Ganeshan
    Domenico De Santis
    Michela Polici
    Carlotta Rucci
    Tiziano Polidori
    Gisella Guido
    Benedetta Bracci
    Antonella Benvenga
    Luca Barbato
    Andrea Laghi
    La radiologia medica, 2021, 126 : 1415 - 1424
  • [44] Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia
    Caruso, Damiano
    Pucciarelli, Francesco
    Zerunian, Marta
    Ganeshan, Balaji
    De Santis, Domenico
    Polici, Michela
    Rucci, Carlotta
    Polidori, Tiziano
    Guido, Gisella
    Bracci, Benedetta
    Benvenga, Antonella
    Barbato, Luca
    Laghi, Andrea
    RADIOLOGIA MEDICA, 2021, 126 (11): : 1415 - 1424
  • [45] Value of Quantitative CTTA in Differentiating Malignant From Benign Bosniak III Renal Lesions on CT Images
    Zhang, Yuhan
    Zhao, Ye
    Lv, Yang
    Gu, Xinquan
    JOURNAL OF COMPUTER ASSISTED TOMOGRAPHY, 2021, 45 (04) : 528 - 536
  • [46] Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning
    Ruben Ngnitewe Massa’a
    Elizabeth M. Stoeckl
    Meghan G. Lubner
    David Smith
    Lu Mao
    Daniel D. Shapiro
    E. Jason Abel
    Andrew L. Wentland
    Abdominal Radiology, 2022, 47 : 2896 - 2904
  • [47] DIFFERENTIATING PANCREATIC MUCINOUS CYSTIC NEO PLASMS FORM SEROUS OLIGO CYSTIC ADENOMAS IN SPECTRAL CT IMAGES USING MACHINE LEARNING ALGORITHMS: A PRELIMINARY STUDY
    Li, Chao
    Lin, Xiao-Zhu
    Wang, Rui
    Hui, Chun
    Lam, Kin-Man
    Zhang, Su
    PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4, 2013, : 271 - 276
  • [48] Differentiation of benign from malignant solid renal lesions with MRI-based radiomics and machine learning
    Massa'a, Ruben Ngnitewe
    Stoeckl, Elizabeth M.
    Lubner, Meghan G.
    Smith, David
    Mao, Lu
    Shapiro, Daniel D.
    Abel, E. Jason
    Wentland, Andrew L.
    ABDOMINAL RADIOLOGY, 2022, 47 (08) : 2896 - 2904
  • [49] Development and validation of a machine learning-based CT radiomics model for differentiation of benign and malignant solid renal tumors
    Bang, S.
    Kwon, H. J.
    Yoon, C. I.
    Rhew, S. A.
    Shin, D.
    Moon, H. W.
    Cho, H. J.
    Ha, U.
    Lee, J. Y.
    Hong, S.
    EUROPEAN UROLOGY, 2023, 83
  • [50] Comparison of six machine learning methods for differentiating benign and malignant thyroid nodules using ultrasonographic characteristics
    Liang, Jianguang
    Pang, Tiantian
    Liu, Weixiang
    Li, Xiaogang
    Huang, Leidan
    Gong, Xuehao
    Diao, Xianfen
    BMC MEDICAL IMAGING, 2023, 23 (01)
12345