Evidence map›Paper›PMID 39320574›Full record

ArticleInsights into imaging2024

A practical risk stratification system based on ultrasonography and clinical characteristics for predicting the malignancy of soft tissue masses.

Ying-Lun Zhang, Meng-Jie Wu, Yu Hu, Xiao-Jing Peng, Qian Ma, Cui-Lian Mao, Ye Dong, Zong-Kai Wei, Ying-Qian Gao, Qi-Yu Yao and 4 more

Abstract read
In one paragraph

Article in Insights into imaging, 2024. 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

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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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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Ying-Lun ZhangDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Meng-Jie WuDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yu HuDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Xiao-Jing PengDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Qian MaDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Cui-Lian MaoDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ye DongDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Zong-Kai WeiDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ying-Qian GaoDepartment of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Qi-Yu YaoDepartment of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Jing YaoDepartment of Ultrasound, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, China.
Xin-Hua YeDepartment of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Ju-Ming Li *Department of Orthopedics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. Lijuming7905@163.com.
Ao Li *Department of Ultrasound, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China. cqh2liao@163.com.ORCID http://orcid.org/0000-0002-0442-7170

Funding

National Nature Science Foundation of China 82371979
6 · The paper itself

Abstract

objectiveTo establish a practical risk stratification system (RSS) based on ultrasonography (US) and clinical characteristics for predicting soft tissue masses (STMs) malignancy.

methodsThis retrospective multicenter study included patients with STMs who underwent US and pathological examinations between April 2018 and April 2023. Chi-square tests and multivariable logistic regression analyses were performed to assess the association of US and clinical characteristics with the malignancy of STMs in the training set. The RSS was constructed based on the scores of risk factors and validated externally.

resultsThe training and validation sets included 1027 STMs (mean age, 50.90 ± 16.64, 442 benign and 585 malignant) and 120 STMs (mean age, 51.93 ± 17.90, 69 benign and 51 malignant), respectively. The RSS was constructed based on three clinical characteristics (age, duration, and history of malignancy) and six US characteristics (size, shape, margin, echogenicity, bone invasion, and vascularity). STMs were assigned to six categories in the RSS, including no abnormal findings, benign, probably benign (fitted probabilities [FP] for malignancy: 0.001-0.008), low suspicion (FP: 0.008-0.365), moderate suspicion (FP: 0.189-0.911), and high suspicion (FP: 0.798-0.999) for malignancy. The RSS displayed good diagnostic performance in the training and validation sets with area under the receiver operating characteristic curve (AUC) values of 0.883 and 0.849, respectively.

conclusionThe practical RSS based on US and clinical characteristics could be useful for predicting STM malignancy, thereby providing the benefit of timely treatment strategy management to STM patients. CRITICAL RELEVANCE STATEMENT: With the help of the RSS, better communication between radiologists and clinicians can be realized, thus facilitating tumor management. KEY POINTS: There is no recognized grading system for STM management. A stratification system based on US and clinical features was built. The system realized great communication between radiologists and clinicians in tumor management.

Indexed as

DiagnosisSoft tissue massUltrasonography

Identifiers

PMID39320574
PMCPMC11424597

What Socratic 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 Socratic graph.