schliessen

Filtern

 

Bibliotheken

Validation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma

Previous recursive partitioning analysis (RPA) of patients with malignant glioma (glioblastoma multiforme [GBM] and anaplastic astrocytoma [AA]) produced six prognostic groups (I–VI) classified by six factors. We sought here to determine whether the classification for GBM could be improved by using... Full description

Journal Title: International Journal of Radiation Oncology Biology, Physics, 01 November 2011, Vol.81(3), pp.623-630
Main Author: Li, Jing
Other Authors: Wang, Meihua , Won, Minhee , Shaw, Edward G , Coughlin, Christopher , Curran, Walter J , Mehta, Minesh P
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0360-3016 ; E-ISSN: 1879-355X ; DOI: 10.1016/j.ijrobp.2010.06.012
Link: https://www.sciencedirect.com/science/article/pii/S0360301610008618
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: elsevier_sdoi_10_1016_j_ijrobp_2010_06_012
title: Validation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma
format: Article
creator:
  • Li, Jing
  • Wang, Meihua
  • Won, Minhee
  • Shaw, Edward G
  • Coughlin, Christopher
  • Curran, Walter J
  • Mehta, Minesh P
subjects:
  • Glioblastoma
  • Prognostic Factors
  • Recursive Partitioning Analysis
  • Rtog
  • Medicine
ispartof: International Journal of Radiation Oncology, Biology, Physics, 01 November 2011, Vol.81(3), pp.623-630
description: Previous recursive partitioning analysis (RPA) of patients with malignant glioma (glioblastoma multiforme [GBM] and anaplastic astrocytoma [AA]) produced six prognostic groups (I–VI) classified by six factors. We sought here to determine whether the classification for GBM could be improved by using an updated Radiation Therapy Oncology Group (RTOG) GBM database excluding AA and by considering additional baseline variables. The new analysis considered 42 baseline variables and 1,672 GBM patients from the expanded RTOG glioma database. Patients receiving radiation only were excluded such that all patients received radiation+carmustine. “Radiation dose received” was replaced with “radiation dose assigned.” The new RPA models were compared with the original model by applying them to a test dataset comprising 488 patients from six other RTOG trials. Fitness of the original and new models was evaluated using explained variation. The original RPA model explained more variations in survival in the test dataset than did the new models (20% vs. 15%) and was therefore chosen for further analysis. It was reduced by combining Classes V and VI to produce three prognostic classes (Classes III, IV, and V+VI), as Classes V and VI had indistinguishable survival in the test dataset. The simplified model did not further improve performance (explained variation 18% vs. 20%) but is easier to apply because it involves only four variables: age, performance status, extent of resection, and neurologic function. Applying this simplified model to the updated GBM database resulted in three distinct classes with median survival times of 17.1, 11.2, and 7.5 months for Classes III, IV, and V+VI, respectively. The final model, the simplified original RPA model combining Classes V and VI, resulted in three distinct prognostic groups defined by age, performance status, extent of resection, and neurologic function. This classification will be used in future RTOG GBM trials.
language: eng
source:
identifier: ISSN: 0360-3016 ; E-ISSN: 1879-355X ; DOI: 10.1016/j.ijrobp.2010.06.012
fulltext: fulltext
issn:
  • 0360-3016
  • 03603016
  • 1879-355X
  • 1879355X
url: Link


@attributes
ID45648468
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordiddoi_10_1016_j_ijrobp_2010_06_012
sourceidelsevier_s
recordidTN_elsevier_sdoi_10_1016_j_ijrobp_2010_06_012
sourcesystemOther
dbid
0--K
1.FO
21B1
31P~
41RT
5457
64G.
77-5
8AAEDT
9AAQFI
10ACIUM
11ADPAM
12AEVXI
13AFTJW
14AITUG
15AJUYK
16ALXNB
17BELOY
18FDB
19GBLVA
20HED
21J1W
22KOM
23OC~
24OO-
25RPZ
26SDG
27SEL
28SSZ
29UV1
30XH2
31Z5R
pqid903659323
galeid269180890
display
typearticle
titleValidation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma
creatorLi, Jing ; Wang, Meihua ; Won, Minhee ; Shaw, Edward G ; Coughlin, Christopher ; Curran, Walter J ; Mehta, Minesh P
ispartofInternational Journal of Radiation Oncology, Biology, Physics, 01 November 2011, Vol.81(3), pp.623-630
identifier
subjectGlioblastoma ; Prognostic Factors ; Recursive Partitioning Analysis ; Rtog ; Medicine
descriptionPrevious recursive partitioning analysis (RPA) of patients with malignant glioma (glioblastoma multiforme [GBM] and anaplastic astrocytoma [AA]) produced six prognostic groups (I–VI) classified by six factors. We sought here to determine whether the classification for GBM could be improved by using an updated Radiation Therapy Oncology Group (RTOG) GBM database excluding AA and by considering additional baseline variables. The new analysis considered 42 baseline variables and 1,672 GBM patients from the expanded RTOG glioma database. Patients receiving radiation only were excluded such that all patients received radiation+carmustine. “Radiation dose received” was replaced with “radiation dose assigned.” The new RPA models were compared with the original model by applying them to a test dataset comprising 488 patients from six other RTOG trials. Fitness of the original and new models was evaluated using explained variation. The original RPA model explained more variations in survival in the test dataset than did the new models (20% vs. 15%) and was therefore chosen for further analysis. It was reduced by combining Classes V and VI to produce three prognostic classes (Classes III, IV, and V+VI), as Classes V and VI had indistinguishable survival in the test dataset. The simplified model did not further improve performance (explained variation 18% vs. 20%) but is easier to apply because it involves only four variables: age, performance status, extent of resection, and neurologic function. Applying this simplified model to the updated GBM database resulted in three distinct classes with median survival times of 17.1, 11.2, and 7.5 months for Classes III, IV, and V+VI, respectively. The final model, the simplified original RPA model combining Classes V and VI, resulted in three distinct prognostic groups defined by age, performance status, extent of resection, and neurologic function. This classification will be used in future RTOG GBM trials.
languageeng
source
version7
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
backlink$$Uhttps://www.sciencedirect.com/science/article/pii/S0360301610008618$$EView_record_in_ScienceDirect_(Access_to_full_text_may_be_restricted)
search
creatorcontrib
0Li, Jing
1Wang, Meihua
2Won, Minhee
3Shaw, Edward G
4Coughlin, Christopher
5Curran, Walter J
6Mehta, Minesh P
titleValidation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma
description
subject
0Glioblastoma
1Prognostic Factors
2Recursive Partitioning Analysis
3Rtog
4Medicine
general
0English
1Elsevier Inc
210.1016/j.ijrobp.2010.06.012
3ScienceDirect (Elsevier B.V.)
4ScienceDirect Journals (Elsevier)
sourceidelsevier_s
recordidelsevier_sdoi_10_1016_j_ijrobp_2010_06_012
issn
00360-3016
103603016
21879-355X
31879355X
rsrctypearticle
creationdate2011
addtitleInternational Journal of Radiation Oncology, Biology, Physics
searchscope
0elsevier_full
1elsevier2
scope
0elsevier_full
1elsevier2
lsr44$$EView_record_in_ScienceDirect_(Access_to_full_text_may_be_restricted)
tmp01ScienceDirect Journals (Elsevier)
tmp02
0--K
1.FO
21B1
31P~
41RT
5457
64G.
77-5
8AAEDT
9AAQFI
10ACIUM
11ADPAM
12AEVXI
13AFTJW
14AITUG
15AJUYK
16ALXNB
17BELOY
18FDB
19GBLVA
20HED
21J1W
22KOM
23OC~
24OO-
25RPZ
26SDG
27SEL
28SSZ
29UV1
30XH2
31Z5R
startdate20111101
enddate20111101
lsr40International Journal of Radiation Oncology, Biology, Physics, 01 November 2011, Vol.81 (3), pp.623-630
doi10.1016/j.ijrobp.2010.06.012
citationpf 623 pt 630 vol 81 issue 3
lsr30VSR-Enriched:[pqid, galeid]
sort
titleValidation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma
authorLi, Jing ; Wang, Meihua ; Won, Minhee ; Shaw, Edward G ; Coughlin, Christopher ; Curran, Walter J ; Mehta, Minesh P
creationdate20111101
lso0120111101
facets
frbrgroupid-1220242362433741318
frbrtype5
newrecords20190904
languageeng
topic
0Glioblastoma
1Prognostic Factors
2Recursive Partitioning Analysis
3Rtog
4Medicine
collectionScienceDirect (Elsevier B.V.)
prefilterarticles
rsrctypearticles
creatorcontrib
0Li, Jing
1Wang, Meihua
2Won, Minhee
3Shaw, Edward G
4Coughlin, Christopher
5Curran, Walter J
6Mehta, Minesh P
jtitleInternational Journal of Radiation Oncology, Biology, Physics
creationdate2011
toplevelpeer_reviewed
delivery
delcategoryRemote Search Resource
fulltextfulltext
addata
aulast
0Li
1Wang
2Won
3Shaw
4Coughlin
5Curran
6Mehta
aufirst
0Jing
1Meihua
2Minhee
3Edward G
4Christopher
5Walter J
6Minesh P
auinitJ
auinit1J
au
0Li, Jing
1Wang, Meihua
2Won, Minhee
3Shaw, Edward G
4Coughlin, Christopher
5Curran, Walter J
6Mehta, Minesh P
atitleValidation and Simplification of the Radiation Therapy Oncology Group Recursive Partitioning Analysis Classification for Glioblastoma
jtitleInternational Journal of Radiation Oncology, Biology, Physics
risdate20111101
volume81
issue3
spage623
epage630
pages623-630
issn0360-3016
eissn1879-355X
formatjournal
genrearticle
ristypeJOUR
abstract
pubElsevier Inc
doi10.1016/j.ijrobp.2010.06.012
lad01International Journal of Radiation Oncology, Biology, Physics
date2011-11-01