Breast Cancer Diagnostic Decisions from Multi-Source Data

Ling Xu; Xiangyun Zeng; Boyuan Xing

doi:10.29271/jcpsp.2025.08.964

Author(s)

Ling Xu Department of Ultrasound Imaging, Yichang Central People's Hospital,Yichang, Hubei,China

Xiangyun Zeng College of Mathematics and Physics, China Three Gorges University,Yichang, Hubei,China

Boyuan Xing Department of Ultrasound Imaging, Yichang Central People's Hospital,Yichang, Hubei,China

Abstract

To evaluate the e cacy of support vector machines (SVM) in diagnostic decisions on the benign or malignant nature of the ultrasound Breast Imaging Reporting and Data System (BI-RADS) category 4 breast nodules in a multi-source diagnostic context. Study Design: An experimental study. Place and Duration of the Study: Department of Ultrasound Imaging, Yichang Central People's Hospital, Yichang, China, from January 2020 to November 2023. Methodology: This study involved patients with ultrasound BI-RADS category 4 breast nodules. Conventional ultrasound diagnostics, S-Detect technology, and medical quasi-intelligent software were used to analyse the pre-treatment ultrasound results with pathological diagnoses serving as the reference standard for accuracy. Principal component analysis (PCA) was applied to extract the principal components from the multi-source breast imaging parameters, which were then integrated with SVM for evaluating its feasibility in classifying category 4 breast nodules as benign or malignant across various breast imaging modalities. Results: PCA extracts two principal components from a 12-dimensional feature parameter matrix measured from the multi-source breast imaging. The SVM, when combined with PCA, demonstrated a high level of reliability in multi-source breast cancer diagnostics, achieving a decision accuracy rate of 94.5%. Conclusion: The integration of SVM with PCA principal component analysis has proven to be highly valuable in the diagnostic decisionmaking process for the multi-source breast cancer diagnostics, o ering a robust method for distinguishing between benign and malignant category 4 breast nodules.

Publication Details

Page(s) 964-968

DOI 10.29271/jcpsp.2025.08.964

Published Journal: Journal of the College of Physicians and Surgeons Pakistan, Volume: 35, Issue: 08, Year: 2025

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Keywords

Principal component analysis, Support Vector Machine, Breast imaging, Multi-source diagnostics

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