Evidence map›Paper›PMID 38780895›Full record

ArticleClinical and experimental medicine2024

The diagnostic value of multimodal imaging based on MR combined with ultrasound in benign and malignant breast diseases.

Dong Bai, Nan Zhou, Xiaofei Liu, Yuanzi Liang, Xiaojun Lu, Jiajun Wang, Lei Liang, Zhiqun Wang

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Article in Clinical and experimental medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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0cells of the map it votes in
5citing papers in PubMed
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1 · What the graph read from it

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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

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Dong Bai *Department of Radiology, Aerospace Center Hospital, Beijing, China.
Nan Zhou *Department of Ultrasound, Aerospace Center Hospital, Beijing, China.
Xiaofei LiuDepartment of Radiology, Liangxiang Hospital, Fangshan District, Beijing, China.
Yuanzi LiangDepartment of Radiology, Aerospace Center Hospital, Beijing, China.
Xiaojun LuDepartment of Radiology, Aerospace Center Hospital, Beijing, China.
Jiajun WangDepartment of Ultrasound, Aerospace Center Hospital, Beijing, China.
Lei LiangDepartment of Ultrasound, Aerospace Center Hospital, Beijing, China. lianglei_csk@126.com.
Zhiqun WangDepartment of Radiology, Aerospace Center Hospital, Beijing, China. 439387525@qq.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We aimed to construct and validate a multimodality MRI combined with ultrasound based on radiomics for the evaluation of benign and malignant breast diseases. The preoperative enhanced MRI and ultrasound images of 131 patients with breast diseases confirmed by pathology in Aerospace Center Hospital from January 2021 to August 2023 were retrospectively analyzed, including 73 benign diseases and 58 malignant diseases. Ultrasound and 3.0 T multiparameter MRI scans were performed in all patients. Then, all the data were divided into training set and validation set in a 7:3 ratio. Regions of interest were drawn layer by layer based on ultrasound and MR enhanced sequences to extract radiomics features. The optimal radiomic features were selected by the best feature screening method. Logistic Regression classifier was used to establish models according to the best features, including ultrasound model, MRI model, ultrasound combined with MRI model. The model efficacy was evaluated by the area under the curve (AUC) of the receiver operating characteristic, sensitivity, specificity, and accuracy. The F-test based on ANOVA was used to screen out 20 best ultrasonic features, 11 best MR Features, and 14 best features from the combined model. Among them, texture features accounted for the largest proportion, accounting for 79%.The ultrasound combined with MR Image fusion model based on logistic regression classifier had the best diagnostic performance. The AUC of the training group and the validation group were 0.92 and 091, the sensitivity was 0.80 and 0.67, the specificity was 0.90 and 0.94, and the accuracy was 0.84 and 0.79, respectively. It was better than the simple ultrasound model (AUC of validation set was 0.82) or the simple MR model (AUC of validation set was 0.85). Compared with the traditional ultrasound or magnetic resonance diagnosis of breast diseases, the multimodal model of MRI combined with ultrasound based on radiomics can more accurately predict the benign and malignant breast diseases, thus providing a better basis for clinical diagnosis and treatment.

Indexed as

Breast NeoplasmsMagnetic Resonance ImagingMultimodal ImagingAdultAgedBreastBreast DiseasesFemaleHumansMiddle AgedRetrospective StudiesROC CurveSensitivity and SpecificityUltrasonographyUltrasonography, MammaryYoung AdultBreast diseaseMagnetic resonance imagingMultimodalRadiomicsUltrasound

Identifiers

PMID38780895
PMCPMC11116236

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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.