Evidence map›Paper›PMID 28331887›Full record

ArticleJournal of medical imaging (Bellingham, Wash.)2017

Android application for determining surgical variables in brain-tumor resection procedures.

Rohan C Vijayan, Reid C Thompson, Lola B Chambless, Peter J Morone, Le He, Logan W Clements, Rebekah H Griesenauer, Hakmook Kang, Michael I Miga

Open access · greenAbstract read
In one paragraph

Article in Journal of medical imaging (Bellingham, Wash.), 2017. 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
0.6field-weighted citation impact, top 31% of its field
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, 5 citations in OpenAlex.

  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

9 authors at 3 institutions in 1 country.

Rohan C VijayanVanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee, United States.
Reid C ThompsonVanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States.
Lola B ChamblessVanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States.
Peter J MoroneVanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States.
Le HeVanderbilt University Medical Center , Department of Neurological Surgery, Nashville, Tennessee, United States.
Logan W ClementsVanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee, United States.
Rebekah H GriesenauerVanderbilt University , Department of Biomedical Engineering, Nashville, Tennessee, United States.
Hakmook KangVanderbilt University Medical Center , Department of Biostatistics, Nashville, Tennessee, United States.
Michael I MigaVanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States; Vanderbilt University Medical Center, Department of Neurological Surgery, Nashville, Tennessee, United States; Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, Tennessee, United States.
Vanderbilt University Medical Center · USVanderbilt University · USNeurological Surgery · US

Funding

Multimodal Registration of the Brain's Cortical SurfaceR01NS049251 · NINDS · VANDERBILT UNIVERSITY · PI GOLBY, ALEXANDRA J, MIGA, MICHAEL IAN · 2005 to 2018
$6.7M
NINDS NIH HHS R01 NS049251
6 · The paper itself

Abstract

The fidelity of image-guided neurosurgical procedures is often compromised due to the mechanical deformations that occur during surgery. In recent work, a framework was developed to predict the extent of this brain shift in brain-tumor resection procedures. The approach uses preoperatively determined surgical variables to predict brain shift and then subsequently corrects the patient's preoperative image volume to more closely match the intraoperative state of the patient's brain. However, a clinical workflow difficulty with the execution of this framework is the preoperative acquisition of surgical variables. To simplify and expedite this process, an Android, Java-based application was developed for tablets to provide neurosurgeons with the ability to manipulate three-dimensional models of the patient's neuroanatomy and determine an expected head orientation, craniotomy size and location, and trajectory to be taken into the tumor. These variables can then be exported for use as inputs to the biomechanical model associated with the correction framework. A multisurgeon, multicase mock trial was conducted to compare the accuracy of the virtual plan to that of a mock physical surgery. It was concluded that the Android application was an accurate, efficient, and timely method for planning surgical variables.

Indexed as

Android applicationbrain shiftfinite elementimage guidanceimage-guided surgeryneurosurgerypreoperative planningresectionsoft tissuetumorworkflow

Identifiers

PMID28331887
PMCPMC5333766
OpenAlexW2593955534

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.