A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI.
biomarker
glioblastoma
machine learning
radiomics
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
04 Nov 2021
04 Nov 2021
Historique:
received:
26
09
2021
revised:
20
10
2021
accepted:
21
10
2021
entrez:
27
11
2021
pubmed:
28
11
2021
medline:
28
11
2021
Statut:
epublish
Résumé
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year. Prediction of survival outcomes in GBM patients could represent a huge step in treatment personalization. The objective of this study was to develop machine learning (ML) algorithms for survival prediction of GBM patient. We identified a radiomic signature on a training-set composed of data from the 2019 BraTS challenge (210 patients) from MRI retrieved at diagnosis. Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival at 9-, 12- and 15-months, respectively. This signature was then validated on an independent cohort of 116 GBM patients with confirmed disease relapse for the prediction of patients surviving less or more than the median OS of 22 months. Our model insured an AUC of 0.71 (0.65 on train). The Kaplan-Meier method showed significant OS difference between groups (log-rank
Identifiants
pubmed: 34829395
pii: diagnostics11112043
doi: 10.3390/diagnostics11112043
pmc: PMC8624566
pii:
doi:
Types de publication
Journal Article
Langues
eng
Références
J Neurooncol. 2016 Sep;129(2):221-30
pubmed: 27444431
Cancer Cell. 2011 Dec 13;20(6):810-7
pubmed: 22137795
Methods. 2021 Apr;188:112-121
pubmed: 32522530
J Cell Physiol. 2018 Jan;233(1):378-386
pubmed: 28266716
Acad Radiol. 2019 Oct;26(10):1292-1300
pubmed: 30660472
Theranostics. 2019 Feb 12;9(5):1303-1322
pubmed: 30867832
Eur Radiol. 2021 Aug;31(8):6408-6409
pubmed: 33693997
Korean J Radiol. 2021 Sep;22(9):1514-1524
pubmed: 34269536
Proc Natl Acad Sci U S A. 2013 Mar 5;110(10):4009-14
pubmed: 23412337
Neuro Oncol. 2016 Mar;18(3):417-25
pubmed: 26188015
J Neurosci Rural Pract. 2017 Oct-Dec;8(4):629-641
pubmed: 29204027
N Engl J Med. 2008 Jul 31;359(5):492-507
pubmed: 18669428
Neuro Oncol. 2018 May 18;20(6):848-857
pubmed: 29036412
Radiology. 2018 Dec;289(3):797-806
pubmed: 30277442
J Clin Oncol. 2007 Sep 10;25(26):4075-83
pubmed: 17827456
Brain Sci. 2018 Jan 16;8(1):
pubmed: 29337870
Front Neurosci. 2020 Apr 29;14:125
pubmed: 32410929
J Neurooncol. 2013 Sep;114(3):309-17
pubmed: 23817809
Lancet. 2018 Aug 4;392(10145):432-446
pubmed: 30060998
N Engl J Med. 2005 Mar 10;352(10):997-1003
pubmed: 15758010
Cancer Cell. 2015 Sep 14;28(3):318-28
pubmed: 26373279
Stat Med. 2011 May 10;30(10):1105-17
pubmed: 21484848
Sci Data. 2017 Sep 05;4:170117
pubmed: 28872634
J Oncol Pract. 2017 Oct;13(10):629-638
pubmed: 29020535
Nat Methods. 2021 Feb;18(2):203-211
pubmed: 33288961
IEEE Trans Med Imaging. 2015 Oct;34(10):1993-2024
pubmed: 25494501
Front Comput Neurosci. 2020 Aug 04;14:61
pubmed: 32848682
Hum Brain Mapp. 2019 Dec 1;40(17):4952-4964
pubmed: 31403237
Cancer Res. 2017 Nov 1;77(21):e104-e107
pubmed: 29092951
Front Comput Neurosci. 2019 Aug 27;13:58
pubmed: 31507398
Lancet Oncol. 2021 Apr;22(4):e136-e172
pubmed: 33676609
Neuro Oncol. 2012 Nov;14 Suppl 5:v1-49
pubmed: 23095881
Surg Oncol. 2018 Dec;27(4):709-714
pubmed: 30449497
Diagnostics (Basel). 2021 Jul 14;11(7):
pubmed: 34359346
Cancer Discov. 2014 Aug;4(8):956-71
pubmed: 24893890
Cancers (Basel). 2020 Oct 04;12(10):
pubmed: 33020420
Neuro Oncol. 2021 Aug 2;23(8):1231-1251
pubmed: 34185076
Cancers (Basel). 2021 Jul 08;13(14):
pubmed: 34298634
Ther Adv Neurol Disord. 2018 Jul 25;11:1756286418790452
pubmed: 30083233