MULTI-CENTER STUDY DEMONSTRATES RADIOMIC TEXTURE FEATURES DERIVED FROM MR PERFUSION IMAGES PREDICT PSEUDOPROGRESSION FROM TRUE PROGRESSION IN GLIOBLASTOMA PATIENTS

被引：0
|
作者：
Elshafeey, Nabil [1 ]
Kotrotsou, Aikaterini [2 ]
Abrol, Srishti [1 ]
Hassan, Islam [1 ]
Hassan, Ahmed [1 ]
Agarwal, Anand [1 ]
Salek, Kamel [1 ]
Bergamaschi, Samuel [3 ]
Moron, Fanny [4 ]
Law, Meng [3 ]
Zinn, Pascal [4 ]
Colen, Rivka [1 ]
机构：
[1] Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA
[2] Univ Texas MD Anderson Canc Ctr, Dept Diagnost Radiol, Houston, TX 77030 USA
[3] Univ South Calif, Los Angeles, CA USA
[4] Baylor Coll Med, Houston, TX 77030 USA
来源：
NEURO-ONCOLOGY | 2017年 / 19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-02
引用
收藏
页码：143 / 143
页数：1
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  • [1] Multicenter study to demonstrate radiomic texture features derived from MR perfusion images of pseudoprogression compared to true progression in glioblastoma patients.
    Elshafeey, Nabil
    Kotrotsou, Aikaterini
    Camejo, Dunia Giniebra
    Abrol, Srishti
    Hassan, Islam
    El Salek, Kamel
    Hassan, Ahmed
    Shaaban, Ahmed
    Bergamaschi, Samuel
    Moron, Fanny E.
    Law, Meng
    Zinn, Pascal
    Colen, Rivka R.
    [J]. JOURNAL OF CLINICAL ONCOLOGY, 2017, 35
  • [2] Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
    Nabil Elshafeey
    Aikaterini Kotrotsou
    Ahmed Hassan
    Nancy Elshafei
    Islam Hassan
    Sara Ahmed
    Srishti Abrol
    Anand Agarwal
    Kamel El Salek
    Samuel Bergamaschi
    Jay Acharya
    Fanny E. Moron
    Meng Law
    Gregory N. Fuller
    Jason T. Huse
    Pascal O. Zinn
    Rivka R. Colen
    [J]. Nature Communications, 10
  • [3] Multicenter study demonstrates radiomic features derived from magnetic resonance perfusion images identify pseudoprogression in glioblastoma
    Elshafeey, Nabil
    Kotrotsou, Aikaterini
    Hassan, Ahmed
    Elshafei, Nancy
    Hassan, Islam
    Ahmed, Sara
    Abrol, Srishti
    Agarwal, Anand
    El Salek, Kamel
    Bergamaschi, Samuel
    Acharya, Jay
    Moron, Fanny E.
    Law, Meng
    Fuller, Gregory N.
    Huse, Jason T.
    Zinn, Pascal O.
    Colen, Rivka R.
    [J]. NATURE COMMUNICATIONS, 2019, 10 (1)
  • [4] DYNAMIC PERFUSION MR IMAGES CAN PREDICT THE PSEUDOPROGRESSION MIMICKING TRUE PROGRESSION IN THE TREATMENT OF NEWLY DIAGNOSED GLIOBLASTOMA
    Kong, Doo-Sik
    Kim, Sung Tae
    Lee, Jung-Il
    Park, Kwan
    Kim, Jong Hyun
    Nam, Do-Hyun
    [J]. NEURO-ONCOLOGY, 2009, 11 (06) : 893 - 893
  • [5] Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study
    Saltybaeva, Natalia
    Tanadini-Lang, Stephanie
    Vuong, Diem
    Burgermeister, Simon
    Mayinger, Michael
    Bink, Andrea
    Andratschke, Nicolaus
    Guckenberger, Matthias
    Bogowicz, Marta
    [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2022, 22 : 131 - 136
  • [6] Radiomic Analysis to Predict Outcome in Recurrent Glioblastoma Based on Multi-Center MR Imaging From the Prospective DIRECTOR Trial
    Vils, Alex
    Bogowicz, Marta
    Tanadini-Lang, Stephanie
    Vuong, Diem
    Saltybaeva, Natalia
    Kraft, Johannes
    Wirsching, Hans-Georg
    Gramatzki, Dorothee
    Wick, Wolfgang
    Rushing, Elisabeth
    Reifenberger, Guido
    Guckenberger, Matthias
    Weller, Michael
    Andratschke, Nicolaus
    [J]. FRONTIERS IN ONCOLOGY, 2021, 11
  • [7] Radiomic Machine Learning Algorithms Discriminate Pseudo-Progression From True Progression in Glioblastoma Patients: A Multi-Institutional Study
    Zinn, Pascal O.
    Abrol, Srishti
    Kotrotsou, Aikaterini
    Hassan, Ahmed
    Elshafeey, Nabil
    Idris, Tagwa
    Manohar, Naveen
    Hassan, Islam
    Salek, Kamel
    Farid, Nikdokht
    McDonald, Carrie
    Weathers, Shiao-Pei
    Bahrami, Naeim
    Bergamaschi, Samuel
    Elakkad, Ahmed
    Alfaro-Munoz, Kristin
    Moron, Fanny
    Huse, Jason
    Weinberg, Jeffrey S.
    Ferguson, Sherise D.
    Kogias, Evangelos
    Heimberger, Amy B.
    Sawaya, Raymond
    Kumar, Ashok M.
    de Groot, John
    Law, Meng
    Colen, Rivka R.
    [J]. NEUROSURGERY, 2018, 65 : 119 - 120
  • [8] Robustness of radiomic features in magnetic resonance imaging for patients with glioblastoma: Multi-center study (vol 22, pg 131, 2022)
    Saltybaeva, Natalia
    Tanadini-Lang, Stephanie
    Vuong, Diem
    Burgermeister, Simon
    Mayinger, Michael
    Bink, Andrea
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    Guckenberger, Matthias
    Bogowicz, Marta
    [J]. PHYSICS & IMAGING IN RADIATION ONCOLOGY, 2022, 23 : 43 - 43
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    Kirschke, Jan S. S.
    Zimmer, Claus
    Wiest, Roland
    Menze, Bjoern H. H.
    [J]. FRONTIERS IN NEUROLOGY, 2023, 14
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    [J]. NEURO-ONCOLOGY, 2021, 23 : 141 - 141
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