MACHINE LEARNING USING MRI RADIOMIC ANALYSIS TO PREDICT KI-67 IN WHO GRADE I MENINGIOMAS

被引：0
|
作者：
Khanna, Omaditya [1 ]
Kazerooni, Anahita Fathi [2 ]
Garcia, Jose A. [2 ]
Sako, Chiharu [2 ]
Arif, Sherjeel [2 ]
Shi, Wenyin [3 ]
Davatzikos, Christos [2 ]
机构：
[1] Thomas Jefferson Univ Hosp, Philadelphia, PA USA
[2] Univ Penn, Philadelphia, PA 19104 USA
[3] Thomas Jefferson Univ, Dept Radiat Oncol, Philadelphia, PA 19107 USA
来源：
NEURO-ONCOLOGY | 2021年 / 23卷
关键词：
D O I：
暂无
中图分类号：
R73 [肿瘤学];
学科分类号：
100214 ;
摘要：
NIMG-63
引用
收藏
页码：143 / 144
页数：2
相关论文
共 50 条
  • [1] Machine Learning Using Multiparametric MRI Radiomic Feature Analysis to Predict Ki-67 in Meningiomas
    Khanna, Omaditya
    Kazerooni, Anahita F.
    Farrell, Christopher J.
    Baldassari, Michael
    Alexander, Tyler
    Karsy, Michael
    Garcia, Jose
    Sako, Chiharu
    Evans, James J.
    Judy, Kevin D.
    Andrews, David W.
    Sharan, Ashwini D.
    Shi, Wenyin
    Davatzikos, Christos
    [J]. NEUROSURGERY, 2022, 68 : 80 - 80
  • [2] Machine Learning Using Multiparametric Magnetic Resonance Imaging Radiomic Feature Analysis to Predict Ki-67 in World Health Organization Grade I Meningiomas
    Khanna, Omaditya
    Kazerooni, Anahita Fathi
    Farrell, Christopher J.
    Baldassari, Michael P.
    Alexander, Tyler D.
    Karsy, Michael
    Greenberger, Benjamin A.
    Garcia, Jose A.
    Sako, Chiharu
    Evans, James J.
    Judy, Kevin D.
    Andrews, David W.
    Flanders, Adam E.
    Sharan, Ashwini D.
    Dicker, Adam P.
    Shi, Wenyin
    Davatzikos, Christos
    [J]. NEUROSURGERY, 2021, 89 (05) : 928 - 936
  • [3] Precision Medicine for Meningiomas: Machine Learning Using Radiomic Feature Analysis Identifies Ki-67 Proliferative Index as a Prognostic Marker of Clinical Outcomes
    Khanna, Omaditya
    Kazerooni, Anahita Fathi
    Arif, Sherjeel
    Baldassari, Michael P.
    Mahtabfar, Aria
    Andrews, Carrie Elizabeth
    Hafazalla, Karim
    Farrell, Christopher James
    Evans, James J.
    Andrews, David W.
    Shi, Wenyin
    Davatzikos, Christos
    [J]. NEUROSURGERY, 2023, 69 : 69 - 69
  • [4] Correlation of Diffusion and Perfusion MRI With Ki-67 in High-Grade Meningiomas
    Ginat, Daniel T.
    Mangla, Rajiv
    Yeaney, Gabrielle
    Wang, Henry Z.
    [J]. AMERICAN JOURNAL OF ROENTGENOLOGY, 2010, 195 (06) : 1391 - 1395
  • [5] Value of Ki-67 Labeling Index in Predicting Recurrence of WHO Grade I Cranial Base Meningiomas
    Matias, Jose Gabrielle
    Jusue-Torres, Ignacio
    Martin, Brendan
    Bajaj, Ankush
    Borys, Ewa
    Melian, Edward
    Barton, Kevin
    Anderson, Douglas E.
    Prabhu, Vikram C.
    [J]. JOURNAL OF NEUROLOGICAL SURGERY PART B-SKULL BASE, 2019, 80 (03) : 287 - 294
  • [6] Radiomic features predict Ki-67 expression level and survival in lower grade gliomas
    Yiming Li
    Zenghui Qian
    Kaibin Xu
    Kai Wang
    Xing Fan
    Shaowu Li
    Xing Liu
    Yinyan Wang
    Tao Jiang
    [J]. Journal of Neuro-Oncology, 2017, 135 : 317 - 324
  • [7] Radiomic features predict Ki-67 expression level and survival in lower grade gliomas
    Li, Yiming
    Qian, Zenghui
    Xu, Kaibin
    Wang, Kai
    Fan, Xing
    Li, Shaowu
    Liu, Xing
    Wang, Yinyan
    Jiang, Tao
    [J]. JOURNAL OF NEURO-ONCOLOGY, 2017, 135 (02) : 317 - 324
  • [8] Small hepatocellular carcinoma: using MRI to predict histological grade and Ki-67 expression
    Li, Y.
    Chen, J.
    Weng, S.
    Sun, H.
    Yan, C.
    Xu, X.
    Ye, R.
    Hong, J.
    [J]. CLINICAL RADIOLOGY, 2019, 74 (08) : 653.e1 - 653.e9
  • [9] Ki-67 immunoreactivity in meningiomas - Determination of the proliferative potential of meningiomas using the monoclonal antibody Ki-67
    Madsen, C
    Schroder, HD
    [J]. CLINICAL NEUROPATHOLOGY, 1997, 16 (03) : 137 - 142
  • [10] Value of KI-67/MIB-1 labeling index and simpson grading system to predict the recurrence of who grade I intracranial meningiomas compared to who grade II
    Prat-Acin, Ricardo
    Juliana Guarin-Corredor, Maria
    Galeano-Senabre, Inma
    Ayuso-Sacido, Angel
    Vera-Sempere, Francisco
    [J]. JOURNAL OF CLINICAL NEUROSCIENCE, 2021, 86 : 32 - 37
12345