Evidence map›Paper›PMID 41593557›Full record

ArticleBMC medical imaging2026

Diagnostic value of ultrasound peritumoral viscoelasticity parameters in breast cancer: enhancing BI-RADS classification performance.

Jiatong Xu, Junni Shi, Yunqian Huang, Chuanjian Chen, Guanghua Xiang, Wen Zheng, Man Chen

Abstract read
In one paragraph

Article in BMC medical imaging, 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

7 authors.

Jiatong Xu *Department of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China.
Junni Shi *Department of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China.
Yunqian HuangDepartment of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China.
Chuanjian ChenDepartment of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China.
Guanghua XiangDepartment of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China.
Wen ZhengDepartment of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China.
Man ChenDepartment of Ultrasound, Tong Ren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111 Xianxia Road, Shanghai, China. maggiech1221@126.com.

Funding

Medical-Engineering Interdisciplinary Program of Donghua University & Shanghai Tongren Hospital 2023DHYGJC-YBA02Shanghai Tongren Hospital Strategic Discipline for Nurturing tx2023xk18
6 · The paper itself

Abstract

objectivesAims to access the diagnostic performance of different regions ultrasound viscoelasticity parameters especially margin of breast cancer and to determine whether the use of margin viscoelasticity can improve its accuracy in the Breast Imaging Reporting and Data System (BI-RADS). MATERIALS &

methods234 benign and 90 malignant lesions were subjected to standard breast ultrasound and viscoelasticity examinations. The doctors selected region of interest (ROI) to measure viscoelasticity. ROI-1, ROI-2, and ROI-3 represent the tumor, peritumoral, and peripheral areas, respectively. The viscosity modulus (VMean, VMin, VMax, VSD) and elasticity modulus (EMean, EMin, EMax, ESD) of 3 ROIs were analyzed. The diagnostic performance of viscoelasticity of three regions was assessed by receiver operating characteristic curves (ROC). Comparison of the effectiveness of B-Mode ultrasound and viscoelastic parameters in BI-RADS diagnosis of breast cancer based on true positive (TP) and false negative (FN).

resultsThe optimal viscoelasticity-related parameters for differentiating breast lesions were determined to be 2-EMax and 2-VMax, with area under the curve (AUC) values of 0.84 (0.79–0.90) and 0.85 (0.80–0.90), respectively. Using ≥ 30.4 kPa and ≥ 3.3 Pa·s as cutoff values, the BI-RADS classification was then modified. The joint model improves diagnoses of benign lesions in category 4 (69/73). In the same way, 2-EMin + 2-VMin can improve the diagnosis rate of benign lesions (227/234). Viscoelastic parameters have better diagnostic performance than viscosity and elasticity alone.

conclusionUltrasound quantitative viscoelasticity parameters of breast mass especially lesion margin can show more comprehensive information. Viscoelasticity improves diagnostic accuracy of BI-RADS categories and reduces unnecessary biopsies.

Indexed as

Breast NeoplasmsElasticity Imaging TechniquesUltrasonography, MammaryAdultAgedElasticityElastic ModulusFemaleHumansMiddle AgedReproducibility of ResultsROC CurveSensitivity and SpecificityViscosityBreast cancerElasticityUltrasoundViscoelasticityViscosity

Identifiers

PMID41593557
PMCPMC12918151

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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.