IMG-43. Non-Invasive Prediction of Malignant Transformation in Grade 2 IDH-Mutant Gliomas Using Radiomics and Machine Learning Article Swipe

PDF

S. Indira Gandhi , An‐Jou Liang , Ashley Teraishi , Jingwen Yao , Albert Lai , Benjamin M. Ellingson ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1093/neuonc/noaf201.1122

Grade 2 IDH mutant gliomas are known to undergo malignant transformation (MT). Early prediction of this transformation remains a significant clinical challenge. Radiomics, the extraction of high-dimensional imaging features, has emerged as a promising tool for non-invasive tumor characterization. Our study looked to implement quantitative radiomics analyses to identify textural information from clinical imaging that might enhance the prediction of MT in grade 2 IDH-mutant gliomas. We conducted a retrospective analysis of institutional (UCLA) and public (UCSF-PDGM) datasets with histologically confirmed IDH-mutant gliomas. Institutional dataset included patients underwent serial magnetic resonance imaging (MRI) and had pathologic clinical progression and/or transformation with at least one repeat biopsy/surgery. Radiomic features were extracted from T1-subtraction maps, T2-weighted images, and apparent diffusion coefficient (ADC) maps from diffusion MRI scans. The T2-hyperintense lesion was segmented, excluding necrosis, using NS-HGlio (Neosoma Inc, Groton, MA). UCLA patients were categorized based on the presence or absence of MT, defined as progression from WHO grade 2 to grade 3 or 4 by histology at repeat biopsy/surgery. Machine learning models, including random forest and support vector machines, were trained on 75% UCSF-PDGM dataset to classify grade 2 versus grade 3/4 tumors, validated on the remaining 25%. The model was used to generate a radiomics risk-score to predict MT in the UCLA dataset, evaluated using the ROC analysis. Model achieved AUC of 0.814 (CI: 0.73--0.90) on the UCSF training set (n = 77). On the UCSF test set (n = 26), sensitivity was 0.93, specificity 0.92, with AUC of 0.92 (CI: 0.80–1.00). Evaluation on the independent dataset of UCLA patients at the second surgical timepoint (n = 59) demonstrated a sensitivity of 0.69, specificity of 0.76, and AUC of 0.73 (CI: 0.61–0.84). Radiomics may enable non-invasive prediction of MT in IDH-mutant gliomas, outperforming conventional clinical models.

Related Topics

Truth And Reconciliation Commission Of Canada

2025 Nba Draft

28 Years Later

Concepts

No concepts available.

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.1093/neuonc/noaf201.1122
PDF: https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf
OA Status: bronze
OpenAlex ID: https://openalex.org/W4416085691

All OpenAlex metadata

Raw OpenAlex JSON

OpenAlex ID: https://openalex.org/W4416085691

Canonical identifier for this work in OpenAlex
DOI: https://doi.org/10.1093/neuonc/noaf201.1122

Digital Object Identifier
Title: IMG-43. Non-Invasive Prediction of Malignant Transformation in Grade 2 IDH-Mutant Gliomas Using Radiomics and Machine Learning

Work title
Type: article

OpenAlex work type
Language: en

Primary language
Publication year: 2025

Year of publication
Publication date: 2025-11-01

Full publication date if available
Authors: S. Indira Gandhi, An‐Jou Liang, Ashley Teraishi, Jingwen Yao, Albert Lai, Benjamin M. Ellingson

List of authors in order
Landing page: https://doi.org/10.1093/neuonc/noaf201.1122

Publisher landing page
PDF URL: https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf

Direct link to full text PDF
Open access: Yes

Whether a free full text is available
OA status: bronze

Open access status per OpenAlex
OA URL: https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf

Direct OA link when available
Cited by: 0

Total citation count in OpenAlex

Full payload

id	https://openalex.org/W4416085691
doi	https://doi.org/10.1093/neuonc/noaf201.1122
ids.doi	https://doi.org/10.1093/neuonc/noaf201.1122
ids.openalex	https://openalex.org/W4416085691
fwci
type	article
title	IMG-43. Non-Invasive Prediction of Malignant Transformation in Grade 2 IDH-Mutant Gliomas Using Radiomics and Machine Learning
biblio.issue	Supplement_5
biblio.volume	27
biblio.last_page	v283
biblio.first_page	v282
is_xpac	False
apc_list.value	4612
apc_list.currency	USD
apc_list.value_usd	4612
apc_paid
language	en
locations[0].id	doi:10.1093/neuonc/noaf201.1122
locations[0].is_oa	True
locations[0].source.id	https://openalex.org/S106908163
locations[0].source.issn	1522-8517, 1523-5866
locations[0].source.type	journal
locations[0].source.is_oa	False
locations[0].source.issn_l	1522-8517
locations[0].source.is_core	True
locations[0].source.is_in_doaj	False
locations[0].source.display_name	Neuro-Oncology
locations[0].source.host_organization	https://openalex.org/P4310311648
locations[0].source.host_organization_name	Oxford University Press
locations[0].source.host_organization_lineage	https://openalex.org/P4310311648, https://openalex.org/P4310311647
locations[0].source.host_organization_lineage_names	Oxford University Press, University of Oxford
locations[0].license
locations[0].pdf_url	https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf
locations[0].version	publishedVersion
locations[0].raw_type	journal-article
locations[0].license_id
locations[0].is_accepted	True
locations[0].is_published	True
locations[0].raw_source_name	Neuro-Oncology
locations[0].landing_page_url	https://doi.org/10.1093/neuonc/noaf201.1122
indexed_in	crossref
authorships[0].author.id	https://openalex.org/A5056776890
authorships[0].author.orcid
authorships[0].author.display_name	S. Indira Gandhi
authorships[0].countries	US
authorships[0].affiliations[0].institution_ids	https://openalex.org/I161318765
authorships[0].affiliations[0].raw_affiliation_string	University of California, Los Angeles, Los Angeles, USA
authorships[0].institutions[0].id	https://openalex.org/I161318765
authorships[0].institutions[0].ror	https://ror.org/046rm7j60
authorships[0].institutions[0].type	education
authorships[0].institutions[0].lineage	https://openalex.org/I161318765
authorships[0].institutions[0].country_code	US
authorships[0].institutions[0].display_name	University of California, Los Angeles
authorships[0].author_position	first
authorships[0].raw_author_name	Sani Gandhi
authorships[0].is_corresponding	False
authorships[0].raw_affiliation_strings	University of California, Los Angeles, Los Angeles, USA
authorships[1].author.id	https://openalex.org/A5045679854
authorships[1].author.orcid	https://orcid.org/0009-0004-5305-830X
authorships[1].author.display_name	An‐Jou Liang
authorships[1].countries	US
authorships[1].affiliations[0].institution_ids	https://openalex.org/I161318765
authorships[1].affiliations[0].raw_affiliation_string	University of California, Los Angeles, Los Angeles, USA
authorships[1].institutions[0].id	https://openalex.org/I161318765
authorships[1].institutions[0].ror	https://ror.org/046rm7j60
authorships[1].institutions[0].type	education
authorships[1].institutions[0].lineage	https://openalex.org/I161318765
authorships[1].institutions[0].country_code	US
authorships[1].institutions[0].display_name	University of California, Los Angeles
authorships[1].author_position	middle
authorships[1].raw_author_name	Andrea Liang
authorships[1].is_corresponding	False
authorships[1].raw_affiliation_strings	University of California, Los Angeles, Los Angeles, USA
authorships[2].author.id	https://openalex.org/A5093242298
authorships[2].author.orcid	https://orcid.org/0009-0004-3442-8109
authorships[2].author.display_name	Ashley Teraishi
authorships[2].countries	US
authorships[2].affiliations[0].institution_ids	https://openalex.org/I161318765
authorships[2].affiliations[0].raw_affiliation_string	University of California, Los Angeles, Los Angeles, USA
authorships[2].institutions[0].id	https://openalex.org/I161318765
authorships[2].institutions[0].ror	https://ror.org/046rm7j60
authorships[2].institutions[0].type	education
authorships[2].institutions[0].lineage	https://openalex.org/I161318765
authorships[2].institutions[0].country_code	US
authorships[2].institutions[0].display_name	University of California, Los Angeles
authorships[2].author_position	middle
authorships[2].raw_author_name	Ashley Teraishi
authorships[2].is_corresponding	False
authorships[2].raw_affiliation_strings	University of California, Los Angeles, Los Angeles, USA
authorships[3].author.id	https://openalex.org/A5030410351
authorships[3].author.orcid	https://orcid.org/0000-0002-8828-1841
authorships[3].author.display_name	Jingwen Yao
authorships[3].countries	US
authorships[3].affiliations[0].institution_ids	https://openalex.org/I161318765
authorships[3].affiliations[0].raw_affiliation_string	University of California, Los Angeles, Los Angeles, USA
authorships[3].institutions[0].id	https://openalex.org/I161318765
authorships[3].institutions[0].ror	https://ror.org/046rm7j60
authorships[3].institutions[0].type	education
authorships[3].institutions[0].lineage	https://openalex.org/I161318765
authorships[3].institutions[0].country_code	US
authorships[3].institutions[0].display_name	University of California, Los Angeles
authorships[3].author_position	middle
authorships[3].raw_author_name	Jingwen Yao
authorships[3].is_corresponding	False
authorships[3].raw_affiliation_strings	University of California, Los Angeles, Los Angeles, USA
authorships[4].author.id	https://openalex.org/A5046758575
authorships[4].author.orcid	https://orcid.org/0000-0002-7265-2986
authorships[4].author.display_name	Albert Lai
authorships[4].countries	US
authorships[4].affiliations[0].institution_ids	https://openalex.org/I161318765
authorships[4].affiliations[0].raw_affiliation_string	University of California, Los Angeles, Los Angeles, USA
authorships[4].institutions[0].id	https://openalex.org/I161318765
authorships[4].institutions[0].ror	https://ror.org/046rm7j60
authorships[4].institutions[0].type	education
authorships[4].institutions[0].lineage	https://openalex.org/I161318765
authorships[4].institutions[0].country_code	US
authorships[4].institutions[0].display_name	University of California, Los Angeles
authorships[4].author_position	middle
authorships[4].raw_author_name	Albert Lai
authorships[4].is_corresponding	False
authorships[4].raw_affiliation_strings	University of California, Los Angeles, Los Angeles, USA
authorships[5].author.id	https://openalex.org/A5025351354
authorships[5].author.orcid	https://orcid.org/0000-0002-2764-6640
authorships[5].author.display_name	Benjamin M. Ellingson
authorships[5].countries	US
authorships[5].affiliations[0].institution_ids	https://openalex.org/I161318765
authorships[5].affiliations[0].raw_affiliation_string	University of California, Los Angeles, Los Angeles, USA
authorships[5].institutions[0].id	https://openalex.org/I161318765
authorships[5].institutions[0].ror	https://ror.org/046rm7j60
authorships[5].institutions[0].type	education
authorships[5].institutions[0].lineage	https://openalex.org/I161318765
authorships[5].institutions[0].country_code	US
authorships[5].institutions[0].display_name	University of California, Los Angeles
authorships[5].author_position	last
authorships[5].raw_author_name	Benjamin Ellingson
authorships[5].is_corresponding	False
authorships[5].raw_affiliation_strings	University of California, Los Angeles, Los Angeles, USA
has_content.pdf	True
has_content.grobid_xml	False
is_paratext	False
open_access.is_oa	True
open_access.oa_url	https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf
open_access.oa_status	bronze
open_access.any_repository_has_fulltext	False
created_date	2025-11-11T00:00:00
display_name	IMG-43. Non-Invasive Prediction of Malignant Transformation in Grade 2 IDH-Mutant Gliomas Using Radiomics and Machine Learning
has_fulltext	False
is_retracted	False
updated_date	2025-11-28T09:28:59.258423
primary_topic
cited_by_count	0
locations_count	1
best_oa_location.id	doi:10.1093/neuonc/noaf201.1122
best_oa_location.is_oa	True
best_oa_location.source.id	https://openalex.org/S106908163
best_oa_location.source.issn	1522-8517, 1523-5866
best_oa_location.source.type	journal
best_oa_location.source.is_oa	False
best_oa_location.source.issn_l	1522-8517
best_oa_location.source.is_core	True
best_oa_location.source.is_in_doaj	False
best_oa_location.source.display_name	Neuro-Oncology
best_oa_location.source.host_organization	https://openalex.org/P4310311648
best_oa_location.source.host_organization_name	Oxford University Press
best_oa_location.source.host_organization_lineage	https://openalex.org/P4310311648, https://openalex.org/P4310311647
best_oa_location.source.host_organization_lineage_names	Oxford University Press, University of Oxford
best_oa_location.license
best_oa_location.pdf_url	https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf
best_oa_location.version	publishedVersion
best_oa_location.raw_type	journal-article
best_oa_location.license_id
best_oa_location.is_accepted	True
best_oa_location.is_published	True
best_oa_location.raw_source_name	Neuro-Oncology
best_oa_location.landing_page_url	https://doi.org/10.1093/neuonc/noaf201.1122
primary_location.id	doi:10.1093/neuonc/noaf201.1122
primary_location.is_oa	True
primary_location.source.id	https://openalex.org/S106908163
primary_location.source.issn	1522-8517, 1523-5866
primary_location.source.type	journal
primary_location.source.is_oa	False
primary_location.source.issn_l	1522-8517
primary_location.source.is_core	True
primary_location.source.is_in_doaj	False
primary_location.source.display_name	Neuro-Oncology
primary_location.source.host_organization	https://openalex.org/P4310311648
primary_location.source.host_organization_name	Oxford University Press
primary_location.source.host_organization_lineage	https://openalex.org/P4310311648, https://openalex.org/P4310311647
primary_location.source.host_organization_lineage_names	Oxford University Press, University of Oxford
primary_location.license
primary_location.pdf_url	https://academic.oup.com/neuro-oncology/article-pdf/27/Supplement_5/v282/65256603/noaf201.1122.pdf
primary_location.version	publishedVersion
primary_location.raw_type	journal-article
primary_location.license_id
primary_location.is_accepted	True
primary_location.is_published	True
primary_location.raw_source_name	Neuro-Oncology
primary_location.landing_page_url	https://doi.org/10.1093/neuonc/noaf201.1122
publication_date	2025-11-01
publication_year	2025
referenced_works_count	0
abstract_inverted_index.2	2, 64, 157, 187
abstract_inverted_index.3	160
abstract_inverted_index.4	162
abstract_inverted_index.=	231, 239, 266
abstract_inverted_index.a	19, 33, 69, 203, 269
abstract_inverted_index.(n	230, 238, 265
abstract_inverted_index.MT	61, 208, 288
abstract_inverted_index.On	233
abstract_inverted_index.We	67
abstract_inverted_index.as	32, 152
abstract_inverted_index.at	102, 165, 260
abstract_inverted_index.by	163
abstract_inverted_index.in	62, 209, 289
abstract_inverted_index.of	15, 26, 60, 72, 149, 221, 248, 257, 271, 274, 278, 287
abstract_inverted_index.on	144, 180, 193, 225, 253
abstract_inverted_index.or	147, 161
abstract_inverted_index.to	8, 43, 48, 158, 184, 201, 206
abstract_inverted_index.3/4	190
abstract_inverted_index.59)	267
abstract_inverted_index.75%	181
abstract_inverted_index.AUC	220, 247, 277
abstract_inverted_index.IDH	3
abstract_inverted_index.MRI	124
abstract_inverted_index.MT,	150
abstract_inverted_index.Our	40
abstract_inverted_index.ROC	216
abstract_inverted_index.The	126, 197
abstract_inverted_index.WHO	155
abstract_inverted_index.and	75, 94, 116, 174, 276
abstract_inverted_index.are	6
abstract_inverted_index.for	36
abstract_inverted_index.had	95
abstract_inverted_index.has	30
abstract_inverted_index.may	283
abstract_inverted_index.one	104
abstract_inverted_index.set	229, 237
abstract_inverted_index.the	24, 58, 145, 194, 210, 215, 226, 234, 254, 261
abstract_inverted_index.was	129, 199, 242
abstract_inverted_index.(CI:	223, 250, 280
abstract_inverted_index.0.73	279
abstract_inverted_index.0.92	249
abstract_inverted_index.25%.	196
abstract_inverted_index.26),	240
abstract_inverted_index.77).	232
abstract_inverted_index.Inc,	136
abstract_inverted_index.MA).	138
abstract_inverted_index.UCLA	139, 211, 258
abstract_inverted_index.UCSF	227, 235
abstract_inverted_index.from	52, 111, 122, 154
abstract_inverted_index.maps	121
abstract_inverted_index.test	236
abstract_inverted_index.that	55
abstract_inverted_index.this	16
abstract_inverted_index.tool	35
abstract_inverted_index.used	200
abstract_inverted_index.were	109, 141, 178
abstract_inverted_index.with	79, 101, 246
abstract_inverted_index.(ADC)	120
abstract_inverted_index.(MRI)	93
abstract_inverted_index.(MT).	12
abstract_inverted_index.0.69,	272
abstract_inverted_index.0.76,	275
abstract_inverted_index.0.814	222
abstract_inverted_index.0.92,	245
abstract_inverted_index.0.93,	243
abstract_inverted_index.Early	13
abstract_inverted_index.Grade	1
abstract_inverted_index.Model	218
abstract_inverted_index.based	143
abstract_inverted_index.grade	63, 156, 159, 186, 189
abstract_inverted_index.known	7
abstract_inverted_index.least	103
abstract_inverted_index.maps,	113
abstract_inverted_index.might	56
abstract_inverted_index.model	198
abstract_inverted_index.study	41
abstract_inverted_index.tumor	38
abstract_inverted_index.using	133, 214
abstract_inverted_index.(UCLA)	74
abstract_inverted_index.and/or	99
abstract_inverted_index.enable	284
abstract_inverted_index.forest	173
abstract_inverted_index.lesion	128
abstract_inverted_index.looked	42
abstract_inverted_index.mutant	4
abstract_inverted_index.public	76
abstract_inverted_index.random	172
abstract_inverted_index.repeat	105, 166
abstract_inverted_index.scans.	125
abstract_inverted_index.second	262
abstract_inverted_index.serial	89
abstract_inverted_index.vector	176
abstract_inverted_index.versus	188
abstract_inverted_index.Groton,	137
abstract_inverted_index.Machine	168
abstract_inverted_index.absence	148
abstract_inverted_index.dataset	85, 183, 256
abstract_inverted_index.defined	151
abstract_inverted_index.emerged	31
abstract_inverted_index.enhance	57
abstract_inverted_index.gliomas	5
abstract_inverted_index.images,	115
abstract_inverted_index.imaging	28, 54, 92
abstract_inverted_index.models,	170
abstract_inverted_index.models.	295
abstract_inverted_index.predict	207
abstract_inverted_index.remains	18
abstract_inverted_index.support	175
abstract_inverted_index.trained	179
abstract_inverted_index.tumors,	191
abstract_inverted_index.undergo	9
abstract_inverted_index.(Neosoma	135
abstract_inverted_index.Abstract	0
abstract_inverted_index.NS-HGlio	134
abstract_inverted_index.Radiomic	107
abstract_inverted_index.achieved	219
abstract_inverted_index.analyses	47
abstract_inverted_index.analysis	71
abstract_inverted_index.apparent	117
abstract_inverted_index.classify	185
abstract_inverted_index.clinical	21, 53, 97, 294
abstract_inverted_index.dataset,	212
abstract_inverted_index.datasets	78
abstract_inverted_index.features	108
abstract_inverted_index.generate	202
abstract_inverted_index.gliomas,	291
abstract_inverted_index.gliomas.	66, 83
abstract_inverted_index.identify	49
abstract_inverted_index.included	86
abstract_inverted_index.learning	169
abstract_inverted_index.magnetic	90
abstract_inverted_index.patients	87, 140, 259
abstract_inverted_index.presence	146
abstract_inverted_index.surgical	263
abstract_inverted_index.textural	50
abstract_inverted_index.training	228
abstract_inverted_index.Radiomics	282
abstract_inverted_index.UCSF-PDGM	182
abstract_inverted_index.analysis.	217
abstract_inverted_index.conducted	68
abstract_inverted_index.confirmed	81
abstract_inverted_index.diffusion	118, 123
abstract_inverted_index.evaluated	213
abstract_inverted_index.excluding	131
abstract_inverted_index.extracted	110
abstract_inverted_index.features,	29
abstract_inverted_index.histology	164
abstract_inverted_index.implement	44
abstract_inverted_index.including	171
abstract_inverted_index.machines,	177
abstract_inverted_index.malignant	10
abstract_inverted_index.necrosis,	132
abstract_inverted_index.promising	34
abstract_inverted_index.radiomics	46, 204
abstract_inverted_index.remaining	195
abstract_inverted_index.resonance	91
abstract_inverted_index.timepoint	264
abstract_inverted_index.underwent	88
abstract_inverted_index.validated	192
abstract_inverted_index.Evaluation	252
abstract_inverted_index.IDH-mutant	65, 82, 290
abstract_inverted_index.Radiomics,	23
abstract_inverted_index.challenge.	22
abstract_inverted_index.extraction	25
abstract_inverted_index.pathologic	96
abstract_inverted_index.prediction	14, 59, 286
abstract_inverted_index.risk-score	205
abstract_inverted_index.segmented,	130
abstract_inverted_index.(UCSF-PDGM)	77
abstract_inverted_index.0.73--0.90)	224
abstract_inverted_index.T2-weighted	114
abstract_inverted_index.categorized	142
abstract_inverted_index.coefficient	119
abstract_inverted_index.independent	255
abstract_inverted_index.information	51
abstract_inverted_index.progression	98, 153
abstract_inverted_index.sensitivity	241, 270
abstract_inverted_index.significant	20
abstract_inverted_index.specificity	244, 273
abstract_inverted_index.conventional	293
abstract_inverted_index.demonstrated	268
abstract_inverted_index.non-invasive	37, 285
abstract_inverted_index.quantitative	45
abstract_inverted_index.0.61–0.84).	281
abstract_inverted_index.0.80–1.00).	251
abstract_inverted_index.Institutional	84
abstract_inverted_index.institutional	73
abstract_inverted_index.outperforming	292
abstract_inverted_index.retrospective	70
abstract_inverted_index.T1-subtraction	112
abstract_inverted_index.histologically	80
abstract_inverted_index.transformation	11, 17, 100
abstract_inverted_index.T2-hyperintense	127
abstract_inverted_index.biopsy/surgery.	106, 167
abstract_inverted_index.high-dimensional	27
abstract_inverted_index.characterization.	39
cited_by_percentile_year
countries_distinct_count	1
institutions_distinct_count	6
citation_normalized_percentile