Evidence map›Paper›PMID 37524245›Full record

ArticleJournal of neuroscience methods2023

Assessing informative tract segmentation and nTMS for pre-operative planning.

Oeslle Lucena, Jose Pedro Lavrador, Hassna Irzan, Carla Semedo, Pedro Borges, Francesco Vergani, Alejandro Granados, Rachel Sparks, Keyoumars Ashkan, Sebastien Ourselin

Abstract read
In one paragraph

Article in Journal of neuroscience methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Diffusion MRI with Machine Learning.Imaging neuroscience (Cambridge, Mass.) · 2024
    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

10 authors.

Oeslle LucenaKing's College London, London, UK. Electronic address: oeslle.lucena@kcl.ac.uk.
Jose Pedro LavradorKing's College London, London, UK.
Hassna IrzanKing's College London, London, UK.
Carla SemedoKing's College London, London, UK.
Pedro BorgesKing's College London, London, UK.
Francesco VerganiKing's College Hospital Foundation Trust, London, UK.
Alejandro GranadosKing's College London, London, UK.
Rachel SparksKing's College London, London, UK.
Keyoumars AshkanKing's College London, London, UK; King's College Hospital Foundation Trust, London, UK.
Sebastien OurselinKing's College London, London, UK.

Funding

Wellcome Trust WT203148/Z/16/ZWellcome Trust WT213038/Z/18/Z
6 · The paper itself

Abstract

backgroundDeep learning-based (DL) methods are the best-performing methods for white matter tract segmentation in anatomically healthy subjects. However, tract annotations are variable or absent in clinical data and manual annotations are especially difficult in patients with tumors where normal anatomy may be distorted. Direct cortical and subcortical stimulation is the gold standard ground truth to determine the cortical and sub-cortical lo- cation of motor-eloquent areas intra-operatively. Nonetheless, this technique is invasive, prolongs the surgical procedure, and may cause patient fatigue. Navigated Transcranial Magnetic Stimulation (nTMS) has a well-established correlation to direct cortical stimulation for motor mapping and the added advantage of being able to be acquired pre-operatively. NEW

methodIn this work, we evaluate the feasibility of using nTMS motor responses as a method to assess corticospinal tract (CST) binary masks and estimated uncertainty generated by a DL-based tract segmentation in patients with diffuse gliomas.

resultsOur results show CST binary masks have a high overlap coefficient (OC) with nTMS response masks. A strong negative correlation is found between estimated uncertainty and nTMS response mask distance to the CST binary mask. COMPARISON WITH EXISTING

methodsWe compare our approach (UncSeg) with the state-of-the-art TractSeg in terms of OC between the CST binary masks and nTMS response masks.

conclusionsIn this study, we demonstrate that estimated uncertainty from UncSeg is a good measure of the agreement between the CST binary masks and nTMS response masks distance to the CST binary mask boundary.

Indexed as

Brain NeoplasmsGliomaBrain MappingDiffusion Tensor ImagingHumansNeuronavigationTranscranial Magnetic StimulationDeep LearningNavigated Transcranial Magnetic Stimulation (nTMS)TractographyTract SegmentationUncertainty Quantification

Identifiers

PMID37524245
PMCPMC10861808

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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.