Evidence map›Paper›PMID 40765095›Full record

ArticleCurrent medical imaging2025

Clinical Value of Nomogram Model based on Multimodality Ultrasound Image Characteristics Differentiating Benign and Malignant Breast Masses.

Jiaxin Yan, Jianting Zheng, Shurong Chen, Jiahua Zhao, Yangfan Han, Bo Liang

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Article in Current medical imaging, 2025. 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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5 · Who and what money

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6 authors.

Jiaxin YanDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, China.
Jianting ZhengDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, China.
Shurong ChenDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, China.
Jiahua ZhaoDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, China.
Yangfan HanDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, China.
Bo LiangDepartment of Medical Ultrasound, Affiliated Hospital 2 of Nantong University, Nantong First People's Hospital, Nantong, Jiangsu Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionFinding a convenient, accurate, and non-invasive method to differentiate between benign and malignant breast masses is especially important for clinical practice, and this study aimed to explore the clinical value of Nomogram model based on multimodality ultrasound image characteristics and clinical baseline data for detecting benign and malignant breast masses.

methodsA retrospective analysis of the clinical data and ultrasound imaging characteristics of 132 patients with breast masses. Data were randomly divided into a training set (92 cases) and a validation set (40 cases) in a ratio of 7:3. Logistic regression was applied to the training set data to analyze risk factors related to malignant breast masses and to construct a Nomogram model. Clinical applicability of the model was evaluated and validated.

resultsIn training set, ROC cure analysis results showed that AUC of Nomogram model constructed with CA15-3, CA125, E DISCUSSION: In this study, we constructed a Nomogram model using risk factors associated with malignant breast masses, and the model showed good clinical applicability in distinguishing benign and malignant breast masses. However, this study is a single-center study, and the sample size of the dataset is relatively small, which, to some extent, limits the breadth and depth of validation.

conclusionThe Nomogram model built on multimodal ultrasound imaging features and clinical data demonstrates a strong discriminative ability for malignant breast masses, allowing patients to achieve a significant net benefit.

Indexed as

Breast NeoplasmsMultimodal ImagingNomogramsUltrasonography, MammaryAdultAgedDiagnosis, DifferentialFemaleHumansMiddle AgedRetrospective StudiesBenign breast massesElasticity scoring.Malignant breast massesMultimodality ultrasound imageNomogram model

Identifiers

PMID40765095
PMCPMC13223494

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