Evidence map›Paper›PMID 34531684›Full record

ArticleCancer management and research2021

Preoperative Visualized Ultrasound Assessment of the Recurrent Laryngeal Nerve in Thyroid Cancer Surgery: Reliability and Risk Features by Imaging.

Yushuang He, Zhihui Li, Yujia Yang, Jianyong Lei, Yulan Peng

Open access · goldAbstract read
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Article in Cancer management and research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 14 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Yushuang HeDepartment of Ultrasound, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, People's Republic of China.
Zhihui LiDepartment of Thyroid Surgery, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, People's Republic of China.
Yujia YangDepartment of Ultrasound, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, People's Republic of China.
Jianyong LeiDepartment of Thyroid Surgery, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, People's Republic of China.
Yulan PengDepartment of Ultrasound, West China Hospital of Sichuan University, Chengdu, Sichuan Province, 610041, People's Republic of China.
Sichuan University · CNWest China Hospital of Sichuan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPreoperative identification and visualization of tumor infiltration of the recurrent laryngeal nerve (RLN) in patients with thyroid cancer is important. The purpose of our study was to evaluate the reliability and feasibility of preoperative assessment by ultrasound and to identify ultrasound imaging features potentially associated with tumor infiltration of the RLN.

methodsIn this prospective study, patients undergoing thyroid cancer surgery at our institution between August 2020 and January 2021 were included, and preoperative ultrasound visualization of the RLN and thyroid lesions was performed. RLN infiltration was also confirmed surgically in all cases. Five patients with enlarged lymph nodes were selected to undergo injection of carbon nanoparticles to confirm the correctness of RLN identification by preoperative ultrasound. The repeatability of RLN assessment by ultrasound was evaluated by comparing the correlation between pre- and intraoperative, intra- and inter-group assessments. Parameters of normal RLNs according to age, sex, and body mass index were established. Finally, ultrasound imaging features of patients with RLN tumor infiltration were analyzed to identify potential risk predictors.

resultsAccording to the ultrasonic assessment, RLNs of 70 patients appeared normal, while 14 of those patients appeared to be infiltrated by tumors. During surgery, the 70 cases of normal RLNs were confirmed, but only 8 of the 14 suspected cases of infiltration were confirmed. In all five patients injected with carbon nanoparticles, the location of RLNs adjacent to the marked lymph nodes observed by surgeons corresponded to the RLN location identified by preoperative ultrasound. The repeatability of RLN estimation varied from moderate to excellent. There were no significant differences in cross-sectional area, width, or thickness of normal RLNs according to age, sex, or body mass index. Indistinct margin with tumor, incontinuous shape as ultrasound features by the analysis of patients with surgically confirmed RLN infiltration were associated with tumor invasion.

conclusionWe show that preoperative ultrasound can be applied to visualize the RLN and may help predict tumor infiltration of the RLN.

Indexed as

recurrent laryngeal nervereliabilityultrasoundvisualization

Identifiers

PMID34531684
PMCPMC8439442
OpenAlexW3198232605

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