Evidence map›Paper›PMID 41891708›Full record

ArticleNeurosurgery2026

Natural Language Processing Methods Automate Molecular Marker Extraction From Glioma Pathology Reports.

Nader I Maarouf, David Reinecke, Andrew Smith, John E Markert, Theodore G Cogan, Xu Han, Anton Alyakin, Daniel Alexander Alber, Minjun Park, Nicolas K Goff and 6 more

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In one paragraph

Article in Neurosurgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Nader I MaaroufDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.ORCID 0009-0006-7842-8762
David ReineckeDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Andrew SmithDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
John E MarkertDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Theodore G CoganDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Xu HanDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Anton AlyakinDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Daniel Alexander AlberDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Minjun ParkDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Nicolas K GoffDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Hannah WeissDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Edward S HarakeDepartment of Neurosurgery, The University of Texas at Austin Dell Medical School, Austin, Texas, USA.
Karen EddyDepartment of Neurosurgery, The University of Texas at Austin Dell Medical School, Austin, Texas, USA.
Todd HollonDepartment of Neurosurgery, The University of Texas at Austin Dell Medical School, Austin, Texas, USA.
Eric K OermannDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.
Daniel A OrringerDepartment of Neurosurgery, New York University Langone Medical Center, New York, New York, USA.ORCID 0000-0001-6427-8376

Funding

Clinical Translation of Stimulated Raman HistologyR01CA226527 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ORRINGER, DANIEL · 2018 to 2025
$4.4M
NCI NIH HHS R01 CA226527NCI NIH HHS R01CA226527
6 · The paper itself

Abstract

BACKGROUND AND

objectivesMolecular markers such as isocitrate dehydrogenase (IDH) and alpha-thalassemia/mental retardation syndrome X-linked (ATRX) status are essential for glioma classification and treatment planning, but their manual extraction from pathology reports creates significant research bottlenecks. This study evaluated 3 Natural Language Processing approaches with increasing computational complexity: deterministic Regular Expressions (RegEx), statistical Term Frequency-Inverse Document Frequency (TF-IDF) with logistic regression, and contextual deep learning Bidirectional Encoder Representations from Transformers (BERT). We address whether more intensive approaches provide sufficient performance benefits over simpler approaches in computational pathology research.

methodsWe analyzed pathology reports from 404 patients with glioma at Institution A and 197 at Institution B for external validation. IDH analysis included 399 (Institution A) and 193 (Institution B) patients; ATRX analysis included 361 and 130 patients, respectively. All approaches underwent identical preprocessing steps, including text normalization, terminology standardization, and context extraction. Performance was evaluated using standard classification metrics and memory usage benchmarks on internal and external validation data sets.

resultsSimpler approaches outperformed more intensive approaches on external validation. For IDH, Regex achieved near-perfect accuracy (99%, area under the curve [AUC] 1.000) and TF-IDF performed exceptionally (94.2%, AUC 0.984), while BlueBERT underperformed (85.2%, AUC 0.934). For ATRX, Regex achieved perfect accuracy (100%, AUC 1.000) and TF-IDF maintained high accuracy (98.0%, AUC 0.998), outperforming BERT-large (84.6%, AUC 0.931). BERT-based approaches required 1825-1953 MB of memory vs Regex (0.82-5.52 MB) and TF-IDF (17.27-34.89 MB).

conclusionSimple Natural Language Processing approaches effectively automate molecular marker extraction from pathology reports with near-perfect accuracy while requiring minimal computational resources. This enables expanded sample sizes in retrospective studies, multi-institutional analyses of rare molecular subgroups, and accelerated biomarker research. Future work will focus on validation across larger data sets, infrastructure integration, and expansion to additional molecular markers.

Indexed as

ATRXBERTEHRIDHNLPRegexTF-IDF

Identifiers

PMID41891708
PMCPMC13540065

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.