Evidence map›Paper›PMID 40965145›Full record

ArticleNeurosurgery2026

Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson Disease.

Alexandra G Roberts, Jinwei Zhang, Ceren Tozlu, Dominick Romano, Sema Akkus, Heejong Kim, Mert R Sabuncu, Pascal Spincemaille, Jianqi Li, Yi Wang and 2 more

Abstract read
PubMed Full text at PMC Publisher
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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

12 authors.

Alexandra G RobertsElectrical and Computer Engineering, Cornell University, Ithaca , New York , USA.ORCID 0000-0002-4000-2102
Jinwei ZhangElectrical and Computer Engineering, Johns Hopkins University, Baltimore , Maryland , USA.
Ceren TozluDepartment of Radiology, Weill Cornell Medicine, New York , New York , USA.ORCID 0000-0002-6828-886
Dominick RomanoDepartment of Radiology, Weill Cornell Medicine, New York , New York , USA.ORCID 0000-0002-8691-406
Sema AkkusDepartment of Neurosurgery, Mount Sinai Hospital, New York , New York , USA.ORCID 0000-0002-8934-2642
Heejong KimDepartment of Radiology, Weill Cornell Medicine, New York , New York , USA.ORCID 0000-0002-9871-9755
Mert R SabuncuElectrical and Computer Engineering, Cornell University, Ithaca , New York , USA.ORCID 0000-0002-7068-719
Pascal SpincemailleDepartment of Radiology, Weill Cornell Medicine, New York , New York , USA.ORCID 0000-0002-8821-1341
Jianqi LiSchool of Physics and Electronic Science, East China Normal University, Shanghai , China.
Yi WangElectrical and Computer Engineering, Cornell University, Ithaca , New York , USA.ORCID 0000-0003-1404-8526
Xi WuDepartment of Neurosurgery, Changhai Hospital, Shanghai , China.
Brian H KopellDepartment of Neurosurgery, Mount Sinai Hospital, New York , New York , USA.

Funding

Quantitative Mapping of Substantia Nigra Iron in Parkinson's DiseaseR01NS095562 · NINDS · WEILL MEDICAL COLL OF CORNELL UNIV · PI SHTILBANS, ALEXANDER, WANG, YI · 2016 to 2022
$3.1M
NINDS NIH HHS R01 NS095562
6 · The paper itself

Abstract

BACKGROUND AND

objectivesParkinson disease (PD) patients with motor complications are often considered for deep brain stimulation (DBS) surgery. Predicting symptom improvement to separate DBS responders and nonresponders remains an unmet need. Currently, DBS candidacy is evaluated using the levodopa challenge test (LCT) to confirm dopamine responsiveness and diagnosis. However, prediction of DBS success by measuring presurgical symptom improvement associated with levodopa dosage changes is highly problematic. Quantitative susceptibility mapping (QSM) is a recently developed MRI method that depicts brain iron distribution. As the substantia nigra and subthalamic nuclei are well visualized, QSM has been used in presurgical planning of DBS. Spatial features resulting from iron distribution in these nuclei have been previously linked with disease progression and motor symptom severity. Given its clear target depiction and prior findings regarding susceptibility and PD, this study demonstrates the technical feasibility of predicting DBS outcomes from presurgical QSM.

methodsA novel presurgical QSM radiomics approach using a regression model is presented to predict DBS outcome according to spatial features in QSM deep gray nuclei. To overcome limited and noisy training data, data augmentation using label noise injection or "compensation" was used to improve outcome prediction of the regression model. The QSM radiomics model was evaluated on 67 patients with PD who underwent DBS at 2 medical centers.

resultsThe QSM radiomics model predicted DBS improvement in the Unified Parkinson Disease Rating Scale at Center 1 and Center 2 with Pearson correlation , ( ) and , ( ), respectively. LCT failed to predict DBS improvement at Center 1 and Center 2 with Pearson correlation ( ) and ( ), respectively.

conclusionQSM radiomics has potential to accurately predict DBS outcome in treating patients with PD, offering a valuable alternative to the time-consuming and low-accuracy LCT.

Indexed as

Brain MappingDeep Brain StimulationMagnetic Resonance ImagingParkinson DiseaseAgedFeasibility StudiesFemaleHumansMaleMiddle AgedRadiomicsSubthalamic NucleusTreatment OutcomeDeep brain stimulationMachine learningParkinson diseaseQuantitative susceptibility mappingRadiomicsRegression

Identifiers

PMID40965145
PMCPMC12969791

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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 Socratic graph.