Evidence map›Paper›PMID 40691370›Full record

ArticleNeurosurgical review2025

Disparities in clinical trial enrollment related to laser interstitial thermal therapy for central nervous system metastases.

Aden P Haskell-Mendoza, Ariel T Gonzalez, Ellery H Reason, Joshua D Jackson, Andreas Seas, Aditya A Mohan, Saif E Zaidi, Emily C Lerner, Ethan S Srinivasan, Claire Bradbury and 3 more

Abstract read
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Article in Neurosurgical review, 2025. 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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0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Aden P Haskell-MendozaDuke University School of Medicine, Durham, NC, USA.
Ariel T GonzalezDuke University School of Medicine, Durham, NC, USA.
Ellery H ReasonDuke University School of Medicine, Durham, NC, USA.
Joshua D JacksonDepartment of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
Andreas SeasDuke University School of Medicine, Durham, NC, USA.
Aditya A MohanDepartment of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, 303 Research Drive, Durham, NC, 27710, USA.
Saif E ZaidiDepartment of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, 303 Research Drive, Durham, NC, 27710, USA.
Emily C LernerDepartment of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, 303 Research Drive, Durham, NC, 27710, USA.
Ethan S SrinivasanDepartment of Neurosurgery, Johns Hopkins University, Baltimore, MD, USA.
Claire BradburyDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
James E HerndonDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
C Rory GoodwinDuke Center for Brain and Spine Metastasis, Department of Neurosurgery, Duke University Medical Center, Durham, NC, USA.
Peter E FecciDepartment of Neurosurgery, Preston Robert Tisch Brain Tumor Center, Duke University Medical Center, 303 Research Drive, Durham, NC, 27710, USA. peter.fecci@duke.edu.ORCID http://orcid.org/0000-0002-2912-8695

Funding

Medical Scientist Training Program Training GrantT32GM145449 · NIGMS · DUKE UNIVERSITY · PI Christopher D Kontos · 2022 to 2026
$6.6M
NIGMS NIH HHS T32 GM145449
6 · The paper itself

Abstract

objectiveLaser interstitial thermal therapy (LITT) is a minimally invasive option for the management of a variety of intracranial pathologies, including radiographically progressive tumor following stereotactic radiosurgery. Although LITT has been increasingly accepted in recent years, little is known regarding selection and outcomes for patients of diverse backgrounds, particularly at centers specializing in central nervous system (CNS) metastases.

methodsPatients receiving their index LITT treatment for brain metastasis at a single center from 2015 to 2023 were retrospectively reviewed. Patient demographics and geospatial data were used to compare differences in receipt of LITT by race or ethnicity, survival outcomes, and clinical trial enrollment.

resultsFrom 2015 to 2023, 137 patients harboring 146 target lesions presented for LITT. One hundred and six patients (77.3%) were non-Hispanic White (NHW); of Hispanic or non-White (HNW) patients, 25 (18.2%) were Black or African American, 5 (3.6%) were Asian, and 1 (0.7%) was Hispanic or Latino. Among HNW patients, women more frequently received LITT (p = 0.022), with skin cancers being a more frequent indication among NHW patients (P = 0.019). NHW patients traveled a median of 62.0 (6.2-1045.9) miles to receive LITT vs. 25.9 (1.31-238.3) miles for HNW patients (p = 0.001). There was parity in procedural outcomes across groups. In multivariate analyses, post-LITT overall survival was predicted by pre-LITT KPS (P = 0.0007) and recurrent tumor on biopsy (P = 0.0002), while probability of clinical trial enrollment was less among those of female sex (P = 0.049), HNW race/ethnicity (P = 0.041), or external referral status (P = 0.035).

conclusionsPatient sex, systemic disease histology, and distance from a treating center may differentially influence presentation for LITT according to underlying race/ethnicity. However, patients successfully treated in multidisciplinary CNS metastasis centers experience excellent post-procedural outcomes. Such centers should enact measures to ensure equitable clinical trial enrollment.

Indexed as

Brain NeoplasmsCentral Nervous System NeoplasmsClinical Trials as TopicHealthcare DisparitiesLaser TherapyPatient SelectionAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedRadiosurgeryRetrospective StudiesTreatment OutcomeAccess to health careBrain metastasesClinical trialsHealth equityLaser interstitial thermal therapy

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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.