Evidence map›Paper›PMID 27499365›Full record

ArticleScientific reports2016

Real-time optical diagnosis of gastric cancer with serosal invasion using multiphoton imaging.

Jun Yan, Yu Zheng, Xiaoling Zheng, Zhangyuanzhu Liu, Wenju Liu, Dexin Chen, Xiaoyu Dong, Kai Li, Xiumin Liu, Gang Chen and 4 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 19% 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

5 citing papers in PubMed, 23 citations in OpenAlex.

  1. Review
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  5. Review
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

14 authors at 5 institutions in 1 country.

Jun YanDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Yu ZhengDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Xiaoling ZhengDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Zhangyuanzhu LiuDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Wenju LiuDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Dexin ChenDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Xiaoyu DongDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Kai LiDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Xiumin LiuDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Gang ChenDepartment of Pathology, Fujian Provincial Tumor Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, 350014, P. R. China.
Jianping LuDepartment of Pathology, Fujian Provincial Tumor Hospital, Teaching Hospital of Fujian Medical University, Fuzhou, Fujian, 350014, P. R. China.
Jianxin ChenFujian Provincial Key Laboratory for Photonics Technology &Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, 350007, P. R. China.
Shuangmu ZhuoFujian Provincial Key Laboratory for Photonics Technology &Key Laboratory of Optoelectronic Science and Technology for Medicine of Ministry of Education, Fujian Normal University, Fuzhou, 350007, P. R. China.
Guoxin LiDepartment of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, 510515, P. R. China.
Nanfang Hospital · CNFujian Normal University · CNSouthern Medical University · CNFujian Medical University · CNFujian Provincial Cancer Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A real-time optical biopsy, which could determine tissue histopathology, would be of extraordinary benefit to staging laparoscopy for gastric cancer with serosal invasion (T4) that requires downstage treatment. We investigated the feasibility of using multiphoton imaging to perform a real-time optical diagnosis of gastric cancer with or without serosal invasion. First, a pilot study was performed to establish the optical diagnostic features of gastric cancer with or without serosal invasion using multiphoton imaging compared with hematoxylin-eosin staining and Masson's trichrome staining. Second, a blinded study was performed to compare the diagnostic sensitivity, specificity, and accuracy of multiphoton imaging and endoscopic ultrasonography (EUS) for T4 gastric cancer. In the pilot study, multiphoton imaging revealed collagen loss and degradation and cellular and nuclear pleomorphism in gastric cancer with serosal invasion. The collagen content in gastric cancer with or without serosal invasion was 0.36 ± 0.18 and 0.79 ± 0.16 (p < 0.001), respectively. In the blinded study, the sensitivity, specificity, and accuracy of EUS and multiphoton imaging for T4 gastric cancer were 70% and 90% (p = 0.029), 66.67% and 96.67% (p = 0.003), and 68.33% and 93.33% (p = 0.001), respectively. It is feasible to use multiphoton imaging to make a real-time optical diagnosis of gastric cancer with or without serosal invasion.

Indexed as

AdultAgedFemaleGastrectomyHumansImaging, Three-DimensionalMaleMicroscopy, Fluorescence, MultiphotonMiddle AgedNeoplasm InvasivenessPilot ProjectsSensitivity and SpecificitySerous MembraneStomach Neoplasms

Identifiers

PMID27499365
PMCPMC4976383
OpenAlexW2522867305

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.