Evidence map›Paper›PMID 37745329›Full record

ArticlemedRxiv : the preprint server for health sciences2024

ReMIND: The Brain Resection Multimodal Imaging Database.

Parikshit Juvekar, Reuben Dorent, Fryderyk Kögl, Erickson Torio, Colton Barr, Laura Rigolo, Colin Galvin, Nick Jowkar, Anees Kazi, Nazim Haouchine and 8 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

5 · Who and what money

Authors and funding

18 authors.

Parikshit JuvekarBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Reuben DorentBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Fryderyk KöglBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Erickson TorioBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Colton BarrBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Laura RigoloBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Colin GalvinBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Nick JowkarBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Anees KaziComputer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Nazim HaouchineBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Harneet CheemaBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Nassir NavabComputer Aided Medical Procedures, Technische Universität München, Munich, Germany.
Steve PieperBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
William M WellsBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Wenya Linda BiBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Alexandra GolbyBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Sarah FriskenBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Tina KapurBrigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.

Funding

Training and DisseminationP41EB015902 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI KIKINIS, RON, WESTIN, CARL-FREDRIK · 2012 to 2022
$20.9M
TRD 3 - Enabling Technologies for Intraprocedural GuidanceP41EB028741 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI Nobuhiko Hata, CLARE M TEMPANY · 2021 to 2026
$10.7M
Ultrasound based neurosurgical navigation with uncertainty visualizationR01EB032387 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI FRISKEN, SARAH, GOLBY, ALEXANDRA J · 2022 to 2025
$2.0M
Continuous Compensation of Brain Shift during NeurosurgeryR01EB027134 · NIBIB · BRIGHAM AND WOMEN'S HOSPITAL · PI FRISKEN, SARAH · 2018 to 2021
$1.8M
NIBIB NIH HHS P41 EB015902NIBIB NIH HHS P41 EB028741NIBIB NIH HHS R01 EB027134NIBIB NIH HHS R01 EB032387
6 · The paper itself

Abstract

The standard of care for brain tumors is maximal safe surgical resection. Neuronavigation augments the surgeon's ability to achieve this but loses validity as surgery progresses due to brain shift. Moreover, gliomas are often indistinguishable from surrounding healthy brain tissue. Intraoperative magnetic resonance imaging (iMRI) and ultrasound (iUS) help visualize the tumor and brain shift. iUS is faster and easier to incorporate into surgical workflows but offers a lower contrast between tumorous and healthy tissues than iMRI. With the success of data-hungry Artificial Intelligence algorithms in medical image analysis, the benefits of sharing well-curated data cannot be overstated. To this end, we provide the largest publicly available MRI and iUS database of surgically treated brain tumors, including gliomas (n=92), metastases (n=11), and others (n=11). This collection contains 369 preoperative MRI series, 320 3D iUS series, 301 iMRI series, and 356 segmentations collected from 114 consecutive patients at a single institution. This database is expected to help brain shift and image analysis research and neurosurgical training in interpreting iUS and iMRI.

Identifiers

PMID37745329
PMCPMC10516086

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.