Evidence map›Paper›PMID 37039950›Full record

Trial reportJournal of digital imaging2023

Comparison Between the Stereoscopic Virtual Reality Display System and Conventional Computed Tomography Workstation in the Diagnosis and Characterization of Cerebral Arteriovenous Malformations.

Xiujuan Liu, Jun Mao, Ning Sun, Xiangrong Yu, Lei Chai, Ye Tian, Jianming Wang, Jianchao Liang, Haiquan Tao, Zhishun Wang and 1 more

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

11 authors.

Xiujuan Liu *Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Jun Mao *Department of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Ning SunEngineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, 210000, Jiangsu, China.
Xiangrong YuDepartment of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Lei ChaiEngineering Research Center of Wideband Wireless Communication Technology, Ministry of Education, Nanjing University of Posts and Telecommunications, Nanjing, 210000, Jiangsu, China.
Ye TianDepartment of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Jianming WangDepartment of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Jianchao LiangDepartment of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China.
Haiquan TaoDepartment of Neurosurgery, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Zhuhai, 519000, Guangdong, China.
Zhishun WangDepartment of Psychiatry, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA. zw2105@cumc.columbia.edu.
Ligong LuDepartment of Radiology, Zhuhai People's Hospital (Zhuhai Hospital Affiliated With Jinan University), Kangning Road, Xiangzhou District, Zhuhai, Guangdong, 519000, China. learninzh@163.com.ORCID 0000-0003-1169-0177

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is difficult to accurately understand the angioarchitecture of cerebral arteriovenous malformations (CAVMs) before surgery using existing imaging methods. This study aimed to evaluate the ability of the stereoscopic virtual reality display system (SVRDS) to display the angioarchitecture of CAVMs by comparing its accuracy with that of the conventional computed tomography workstation (CCTW). Nineteen patients with CAVM confirmed on digital subtraction angiography (DSA) or during surgery were studied. Computed tomography angiography images in the SVRDS and CCTW were retrospectively analyzed by two experienced neuroradiologists using a double-blind method. Angioarchitectural parameters, such as the location and size of the nidus, type and number of the arterial feeders and draining veins, and draining pattern of the vessels, were recorded and compared. The diameter of the nidus ranged from 1.1 to 9 cm. Both CCTW and SVRDS correctly diagnosed the location of the nidus in 19 patients with CAVM. Among the 19 patients, 35 arterial feeders and 25 draining veins were confirmed on DSA and during surgery. With the DSA and intraoperative results as the gold standard bases, the CCTW misjudged one arterial feeder and one draining vein and missed three arterial feeders and two draining veins; meanwhile, the SVRDS missed only two arterial feeders. SVRDS had some advantages in displaying nidus, arterial branches, and draining veins of the CAVM compared with CCTW, as well as SVRDS could more intuitively display the overall angio-architectural spatial picture of CAVM.

Indexed as

Intracranial Arteriovenous MalformationsVirtual RealityAngiography, Digital SubtractionCerebral AngiographyHumansRetrospective StudiesTomography, X-Ray ComputedCerebral arteriovenous malformationCT angiographyCT workstationDigital subtraction angiographyVirtual reality

Identifiers

PMID37039950
PMCPMC10406736

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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.