Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma | Zendy

Anahita Fathi Kazerooni | Zendy; Hamed Akbari | Zendy; Garima Shukla | Zendy; Chaitra Badve | Zendy; Jeffrey D. Rudie | Zendy; Chiharu Sako | Zendy; Saima Rathore | Zendy; Spyridon Bakas | Zendy; Sarthak Pati | Zendy; Ashish Singh | Zendy; Mark Bergman | Zendy; Sung Min Ha | Zendy; Despina Kontos | Zendy; MacLean P. Nasrallah | Zendy; Stephen Bagley | Zendy; Robert A. Lustig | Zendy; Donald M. O’Rourke | Zendy; Andrew E. Sloan | Zendy; Jill S. BarnholtzSloan | Zendy; Suyash Mohan | Zendy; Michel Bilello | Zendy; Christos Davatzikos | Zendy

Open Access

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

Author(s) -

Anahita Fathi Kazerooni,

Hamed Akbari,

Garima Shukla,

Chaitra Badve,

Jeffrey D. Rudie,

Chiharu Sako,

Saima Rathore,

Spyridon Bakas,

Sarthak Pati,

Ashish Singh,

Mark Bergman,

Sung Min Ha,

Despina Kontos,

MacLean P. Nasrallah,

Stephen Bagley,

Robert A. Lustig,

Donald M. O’Rourke,

Andrew E. Sloan,

Jill S. BarnholtzSloan,

Suyash Mohan,

Michel Bilello,

Christos Davatzikos

Publication year - 2020

Publication title -

jco clinical cancer informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.188

H-Index - 12

ISSN - 2473-4276

DOI - 10.1200/cci.19.00121

Subject(s) - phenomics , glioblastoma , progression free survival , cancer , medicine , oncology , overall survival , radiology , cancer research , biology , biochemistry , genomics , genome , gene

To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.

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