Evidence map›Paper›PMID 32543025›Full record

ArticleCancer medicine2020

The clinical application value of mixed-reality-assisted surgical navigation for laparoscopic nephrectomy.

Guan Li, Jie Dong, Jinbao Wang, Dongbing Cao, Xin Zhang, Zhiqiang Cao, Guangming Lu

Open access · goldAbstract read
In one paragraph

Article in Cancer medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 5 pooled it
2.7field-weighted citation impact, top 8% 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

25 citing papers in PubMed, 5 syntheses or guidelines pooled it, 38 citations in OpenAlex.

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  4. Visual extended reality tools in image-guided surgery in urology: a systematic review.European journal of nuclear medicine and molecular imaging · 2024
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  20. Applications of Mixed Reality Technology in Orthopedics Surgery: A Pilot Study.Frontiers in bioengineering and biotechnology · 2022
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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

7 authors at 4 institutions in 1 country.

Guan LiDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Jie DongDepartment of Urology, Jinling Hospital, Nanjing Medical University, Nanjing, China.
Jinbao WangDepartment of Radiology, General Hospital of Northern Theater Command, Shenyang, China.
Dongbing CaoDepartment of Urology, Cancer Hospital of China Medical University, Shenyang, China.
Xin ZhangDepartment of Radiology, The First Affiliated Hospital of China Medical University, Shenyang, China.
Zhiqiang CaoDepartment of Urology, General Hospital of Northern Theater Command, Shenyang, China.
Guangming LuDepartment of Radiology, Jinling Hospital, Nanjing Medical University, Nanjing, China.ORCID 0000-0003-4913-2314
Nanjing General Hospital of Nanjing Military Command · CNGeneral Hospital of Shenyang Military Region · CNChina Medical University · CNFirst Hospital of China Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeLaparoscopic nephrectomy (LN) has become the preferred method for renal cell carcinoma (RCC). Adequate preoperative assessment or intraoperative navigation is key to the successful implementation of LN. The aim of this study was to evaluate the clinical application value of mixed-reality-assisted surgical navigation (MRASN) in LN. PATIENTS AND

methodsA total of 100 patients with stage T1N0M0 renal tumors who underwent laparoscopic partial nephrectomy (LPN) or laparoscopic radical nephrectomy (LRN) were prospectively enrolled and divided into a mixed-reality-assisted laparoscopic nephrectomy (MRALN) group (n = 50) and a non-mixed-reality-assisted laparoscopic nephrectomy (non-MRALN) group (n = 50). All patients underwent renal contrast-enhanced CT scans. The CT DICOM data of all patients in the MRALN group were imported into the mixed-reality (MR) postprocessing workstation and underwent holographic three-dimensional visualization (V3D) modeling and MR displayed, respectively. We adopted the Likert scale to evaluate the clinical application value of MRASN. The consistency of evaluators was assessed using the Cohen kappa coefficient (k).

resultsNo significant differences in patient demographic indicators between the MRALN group and the non-MRALN group (P > .05). The subjective score of MRASN clinical application value in operative plan formulation, intraoperative navigation, remote consultation, teaching guidance, and doctor-patient communication were higher in the MRASN group than in the non-MRASN group (all P < .001). There were significantly more patients for whom LPN was successfully implemented in the MRALN group than in the non-MRALN group (82% vs 46%, P < .001). The MRALN group had a shorter operative time (OT) and warm ischemia time (WIT) and less estimated blood loss (EBL) than the non-MRALN group (all P < .001).

conclusionMRASN is helpful for operative plan formulation, intraoperative navigation, remote consultation, teaching guidance, and doctor-patient communication. MRALN may effectively improve the successful implementation rate of LPN and reduce the OT, WIT, and EBL.

Indexed as

AdultAgedFemaleHumansLaparoscopyMaleMiddle AgedNephrectomycomputer tomographylaparoscopic nephrectomymixed realityrenal cell carcinomasurgical navigation

Identifiers

PMID32543025
PMCPMC7402835
OpenAlexW3034673443

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.