Evidence map›Paper›PMID 40385830›Full record

ArticleCureus2025

Efficacy and Safety of Robotic Surgery With Sentinel Lymph Node Navigation in Obese Patients With Endometrial Cancer: A Retrospective Study.

Nozomi Fufuzono, Shinichi Togami, Mika Fukuda, Hiroaki Kobayashi

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Nozomi FufuzonoDepartment of Obstetrics and Gynecology, Faculty of Medicine, Kagoshima University, Kagoshima, JPN.
Shinichi TogamiDepartment of Obstetrics and Gynecology, Faculty of Medicine, Kagoshima University, Kagoshima, JPN.
Mika FukudaDepartment of Obstetrics and Gynecology, Faculty of Medicine, Kagoshima University, Kagoshima, JPN.
Hiroaki KobayashiDepartment of Obstetrics and Gynecology, Faculty of Medicine, Kagoshima University, Kagoshima, JPN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the feasibility and safety of robotic-assisted surgery combined with sentinel lymph node navigation surgery (SNNS) in obese patients with International Federation of Gynecology and Obstetrics stage IA endometrial cancer, comparing surgical and oncologic outcomes between obese and non-obese patients.

methodsThis retrospective study evaluated patients who underwent robotic-assisted hysterectomy, bilateral salpingo-oophorectomy, and SNNS at Kagoshima University Hospital between July 2018 and December 2024. The surgical outcomes, including operative time, console time, blood loss, and postoperative complications, of patients with a body mass index (BMI) of ≥ 30 kg/m² and ≥ 35 kg/m² were compared with those of their non-obese counterparts.

resultsAmong the 294 patients evaluated, 39.5% had BMI ≥ 30 kg/m². Compared to non-obese patients, obese patients had significantly longer surgery start to roll-in time (P = 0.0026) and higher intraoperative blood loss (P = 0.018), whereas no significant differences were observed in operative time, console time, or postoperative complications. Patients with BMI ≥ 35 kg/m² had significantly longer operative time (P = 0.03) and surgery start to roll-in time (P = 0.0039) but showed no increased risk of postoperative complications. No cases required conversion to laparotomy.

conclusionsRobotic surgery with SNNS has minimal impact on the surgical outcomes of obese patients with endometrial cancer. Although obesity influences preoperative setup time and intraoperative blood loss, overall surgical safety is maintained. Thus, robotic surgery with SNNS is feasible and safe for these patients.

Indexed as

endometrial cancerobesityrobotic surgerysentinel lymph nodesurgical outcomes

Identifiers

PMID40385830
PMCPMC12083743

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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.