Evidence map›Paper›PMID 41485095›Full record

ArticleCancer imaging : the official publication of the International Cancer Imaging Society2026

Contrast-enhanced ultrasound quantitative parameters for predicting benign and malignant greater omentum lesions: an applied study.

Yuan Cheng, Zhiguang Chen, Yuting Yang, Xuemei Wang, Yanjun Liu

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Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Yuan ChengDepartment of Ultrasound, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, China.
Zhiguang ChenDepartment of Ultrasound, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, China.
Yuting YangDepartment of Ultrasound, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, China.
Xuemei WangDepartment of Ultrasound, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, China.
Yanjun LiuDepartment of Ultrasound, The First Hospital of China Medical University, No. 155, Nanjing North Street, Heping District, Shenyang, Liaoning Province, 110001, China. lyj7512cmu@163.com.

Funding

China Ultrasound Physician Technology Rising Star Program (No. KJXX2024006)
6 · The paper itself

Abstract

objectiveTo explore the value of contrast-enhanced ultrasound (CEUS) quantitative parameters in differentiating benign and malignant diseases of the greater omentum and to establish a new CEUS Greater Omentum Imaging Reporting and Data System (CEUS-GOIRADS).

methodsWe analyzed 15 CEUS parameters from 124 patients undergoing omental biopsy (2020–2024) to develop a noninvasive predictive model and assess its diagnostic value. Statistically significant parameters were further analyzed using receiver operating characteristic (ROC) curve cutoff values to determine odds ratios (ORs) for each group. A CEUS-OS was developed for each patient, and ROC curve analysis was performed to evaluate its efficacy in distinguishing between benign and malignant omental diseases.

resultsAmong 124 patients meeting the inclusion criteria, 76 had malignant lesions and 48 had benign lesions. Multivariate analysis identified three out of 15 CEUS parameters—IMAX, Fs50, and WioAUC—as associated with malignant lesions of the greater omentum. The noninvasive predictive model demonstrated a sensitivity of 90.7% and specificity of 90.5% for diagnosing malignant omental lesions. Furthermore, the established CEUS-GOIRADS grading system showed a sensitivity of 94.7% and specificity of 89.6% for differentiating between benign and malignant omental diseases.

conclusionQuantitative analysis of CEUS parameters provides effective indicators for determining the nature of greater omental lesions. This non-invasive imaging modality can reliably predict whether omental lesions are benign or malignant. The CEUS-GOIRADS demonstrates high sensitivity and specificity in differentiating benign from malignant omental diseases, suggesting its potential as a CEUS-GOIRADS valuable tool in the multi-step diagnostic evaluation of patients with various omental pathologies.

Indexed as

Contrast MediaOmentumPeritoneal NeoplasmsAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedPredictive Value of TestsSensitivity and SpecificityUltrasonographyContrast MediaContrast-enhanced ultrasoundData systemsOmental lesionsQuantitative analysisTime intensity curve (TIC) analysis

Identifiers

PMID41485095
PMCPMC12866184

What Socratic holds

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LicenceCC BY-NC-ND
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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.