Evidence map›Paper›PMID 38245649›Full record

ArticleAnnals of surgical oncology2024

Comparison of Postoperative Outcomes Between Near-Infrared Fluorescent Imaging-Guided Mediastinal Lymphadenectomy and Conventional Surgery for Esophageal Cancer.

Yong Ao, LeQi Zhong, KongJia Luo, ShiNing Li, Xin Zhang, Lu Shao, XiaoDan Lin, Yi Hu

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Annals of surgical oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
2.9field-weighted citation impact, top 11% 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 5 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Article
  4. 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

8 authors at 2 institutions in 1 country.

Yong Ao *Department of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China.
LeQi Zhong *Department of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China.
KongJia Luo *Department of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China.
ShiNing LiDepartment of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China.
Xin ZhangDepartment of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China.
Lu ShaoSchool of Nursing, Sun Yat-sen University, Guangzhou, China.
XiaoDan LinDepartment of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China.
Yi HuDepartment of Thoracic Oncology, Sun Yat-Sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong Province, China. huyisci@163.com.
Sun Yat-sen University · CNState Key Laboratory of Oncology in South China

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe study aimed to evaluate the efficacy of using near-infrared fluorescent imaging (NIRF) imaging with indocyanine green as an intraoperative tool for achieving complete mediastinal lymph node (LN) resection. PATIENTS AND

methodsBetween September 2019 and July 2021, patients with potential for esophagectomy due to middle and lower thoracic esophageal cancer were enrolled in this study. All patients were scheduled for NIRF-guided mediastinal lymphadenectomy during esophageal cancer surgery and were appropriately assigned to the NIRF group. Patients who underwent esophagectomy between September 2017 and September 2019 were assigned to the historical control group upon satisfying the inclusion/exclusion criteria. Surgical outcomes and the number of removed LNs were compared between the two groups using 1:1 propensity score matching.

resultsOf 67 eligible patients, 59 patients were included in the NIRF group after postsurgical exclusions. The operative time was significantly shorter in the NIRF group than in the historical control group [180 (140-420) min versus 202 (137-338) min; P < 0.001]. The incidence of postoperative chylothorax and hoarseness were significantly lower in the NIRF group than in the historical control group (0% versus 10.2 %; P = 0.036, 3.4% versus 13.6%; P = 0.047). The number of dissected total LNs, mediastinal LNs, and negative LNs was significantly larger in the NIRF group than in the historical control group. The number of overall metastatic LNs and abdominal LNs was comparable between the two groups.

conclusionsNIRF imaging can assist in the thorough and complete mediastinal LNs dissections without increasing complications in patients undergoing esophagectomy.

Indexed as

Esophageal NeoplasmsEsophagectomyIndocyanine GreenLymph Node ExcisionMediastinumAgedFemaleFollow-Up StudiesHumansMaleMiddle AgedOptical ImagingPostoperative ComplicationsPrognosisSpectroscopy, Near-InfraredSurgery, Computer-AssistedIndocyanine Green

Identifiers

PMID38245649
OpenAlexW4391057959

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.