Open AccessCancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma
Author(s) -
Anahita Fathi Kazerooni,
Hamed Akbari,
Gaurav 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) - medicine , magnetic resonance imaging , cohort , glioblastoma , progression free survival , radiology , receiver operating characteristic , oncology , overall survival , cancer research
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.