Evidence map›Paper›PMID 25484943›Full record

ArticleJournal of robotic surgery2007

A neurosurgical navigation system based on intraoperative tumour remnant estimation.

Jaesung Hong, Yoshihiro Muragaki, Ryoichi Nakamura, Makoto Hashizume, Hiroshi Iseki

Open access · hybridAbstract read
In one paragraph

Article in Journal of robotic surgery, 2007. 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, top 94% 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

2 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  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

5 authors at 3 institutions in 1 country.

Jaesung HongDepartment of Nanobiomedicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Japan.
Yoshihiro MuragakiInstitute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.
Ryoichi NakamuraInstitute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.
Makoto HashizumeDepartment of Advanced Medicine and Innovative Technology, Kyushu University Hospital, Fukuoka, Japan.
Hiroshi IsekiInstitute of Advanced Biomedical Engineering and Science, Tokyo Women's Medical University, Tokyo, Japan.
Tokyo Women's Medical University · JPKyushu University · JPKyushu University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper proposes a method to intra-operatively visualize the process of tumour resection until complete resection is accomplished. A fuzzy connectedness method that is robust against image noises was used to identify the tumour position and volume. Based on the tumour segmentation results, the removed area and the residual tumour tissues were examined with reference to the electrocautery trace log. Unique processes that are specific to glioma resection were introduced in the method to improve the accuracy of estimation. Invalid data in the trace log were excluded, and the tumour region surrounded by valid log points was included in the removed area. The proposed system also produces an alarm to indicate whether the electrocautery is being accurately performed within the tumour area. Thus, this surgical navigation system can assist surgeons in intuitively monitoring the tumour resection process and properly removing tumour remnants. Preliminary experiments and a clinical pilot study showed the feasible application of this method.

Indexed as

Display of residual tumourImage-guided surgerySurgical navigationTrace analysis of electrocauteryTumour segmentation

Identifiers

PMID25484943
PMCPMC4247471
OpenAlexW1981007631

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.