Evidence map›Paper›PMID 34003129›Full record

ArticleDiagnostic and interventional radiology (Ankara, Turkey)2021

Prediction of lymph node metastasis in rectal cancer: comparison between shear-wave elastography based ultrasomics and MRI.

Meng-Fei Xian, Xin Zheng, Jian-Bo Xu, Xin Li, Li-Da Chen, Wei Wang

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Article in Diagnostic and interventional radiology (Ankara, Turkey), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

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0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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  4. Machine learning prediction model for lateral lymph node metastasis in rectal cancer.Frontiers in gastroenterology (Lausanne, Switzerland) · 2025
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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Meng-Fei XianDepartment of Medical Ultrasounics, East division of the First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
Xin ZhengDepartment of Medical Ultrasonics, Ultrasonics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Jian-Bo XuDepartment of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Xin LiResearch Center of GE Healthcare, Shanghai., China.
Li-Da ChenDepartment of Medical Ultrasonics, Ultrasonics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
Wei WangDepartment of Medical Ultrasonics, Ultrasonics Artificial Intelligence X-Lab, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe aimed to explore the diagnostic efficiency of shear-wave elastography (SWE) ultrasomics in the preoperative prediction of lymph node (LN) metastasis in rectal cancer.

methodsThis study included 87 patients with pathologically confirmed rectal cancer, with data gathered from August 2017 to August 2018. A total of 1044 ultrasomics features of rectal tumor were collected with AK software from the SWE examinations. The least absolute shrinkage and selection operator (LASSO) regression model was used for feature selection and building a SWE ultrasomics signature. The diagnostic performance was evaluated with an area under the receiver operating characteristic curve (AUC) analysis. Then, the diagnostic performance of the SWE ultrasomics signature was compared with magnetic resonance imaging (MRI).

resultsOf the 87 patients, 40 (46.0%) had LN metastasis. Thirteen ultrasomics features of rectal tumor were selected as the most significant features. The SWE ultrasomics signature correlated with LN metastasis (p < 0.001). Patients with LN metastasis had higher signature than patients without LN metastasis. In terms of diagnostic performance, SWE ultrasomics signature was significantly superior to MRI (AUC, 0.883 vs. 0.760, p = 0.034). The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SWE ultrasomics signature were 82.8%, 87.5%, 78.8%, 77.8%, and 88.1%, respectively, while those of MRI were 75.9%, 77.5%, 74.5%, 72.1%, and 79.6%, respectively.

conclusionSWE ultrasomics is a more accurate predictive method for identifying LN metastasis preoperatively than MRI. Thus, SWE ultrasomics might be used to better guide preoperative individual therapies for patients with rectal cancer.

Indexed as

Elasticity Imaging TechniquesRectal NeoplasmsHumansLymphatic MetastasisLymph NodesMagnetic Resonance ImagingNeoplasm Staging

Identifiers

PMID34003129
PMCPMC8136530

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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.