Article

Multimodal Ultrasound Combined with Machine Learning for Diagnosing Breast Lesions: Incremental Value Assessment of Ultrasound Super-Resolution Imaging

2026-08-20

Abstract excerpt

<title>Abstract</title> <p> <bold>Background.</bold> To integrate conventional ultrasound, contrast-enhanced ultrasound (CEUS), and ultrasound super-resolution imaging (US SRI) into an interpretable machine learning model to distinguish benign from malignant breast lesions. <bold>Methods.</bold> We retrospectively enrolled 210 cases (106 benign, 104 malignant). Following feature selection, six models were emp...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: d4b9b337-f788-5387-9fcf-a53b46fc30ff
DOI: 10.21203/rs.3.rs-10399234/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Multimodal Ultrasound Combined with Machine Learning for Diagnosing Breast Lesions: Incremental Value Assessment of Ultrasound Super-Resolution ImagingDOI 10.21203/rs.3.rs-10399234/v1Read

Development and Validation of a CEUS-Based Intratumoral–Peritumoral Radiomics Nomogram for Preoperative Prediction of HER2-Low Breast CancerSemantic proximity Read

Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers.Semantic proximity Read

Transformer-based Deep Learning Model Using Contrast-enhanced US for Predicting Malignancy in Breast Nodules: A Two-center StudySemantic proximity Read

Development and validation of a multimodal interpretable machine learning model with SHAP for malignancy risk prediction in Bethesda III thyroid nodules: a dual-center retrospective cohort studySemantic proximity Read

Beyond the Tumor: Multi-region Radiomic and Deep Feature Analysis of Tumor, Peritumoral, and Posterior Imaging Features from Mammography and Ultrasound in Differentiating Invasive Micropapillary Carcinoma from Invasive Carcinoma of No Special TypeSemantic proximity Read

Deep Learning for Predicting Breast Cancer: A Systematic Review of Progress and Future DirectionsSemantic proximity Read

WITHDRAWN: Predicting Malignancy in Solid Adnexal Masses: An Externally Validated Machine Learning Model Integrating Conventional and Contrast-Enhanced UltrasoundSemantic proximity Read

Ultrasound Characteristics in Predicting Molecular Subtypes of Breast Cancer: A Retrospective AnalysisSemantic proximity Read

Integrating clinical data and ultrasonographic imaging for non-invasive prediction of HER2 status in breast cancerSemantic proximity Read

Evaluating Machine Learning Classifiers in Breast Cancer: Non-Linear Contributions of MR Diffusion-Perfusion Features to Molecular-based Prognostic StratificationSemantic proximity Read

Multimodal Fusion of Radiomics, 2D Deep Learning, and Clinical Risk Factors for Preoperative Prediction of Lymphovascular Invasion in Breast Cancer: A Multicenter External Validation StudySemantic proximity Read

Multimodal automated diagnosis of lymphovascular invasion in breast cancer on contrast-enhanced MRI: ResUNet++ segmentation and Transformer-based classificationSemantic proximity Read