Kernel extreme learning machine in diagnosis of benign and malignant mass-like breast lesions
10.13929/j.1003-3289.201811041
- Author:
Di YANG
1
Author Information
1. Department of Radiology, Tangdu Hospital, Air Force Military Medical University
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Diagnosis;
differential;
Kernel extreme learning machine;
Magnetic resonance imaging
- From:
Chinese Journal of Medical Imaging Technology
2019;35(4):507-510
- CountryChina
- Language:Chinese
-
Abstract:
Objective To classify benign and malignant breast mass-like lesions by using kernel extreme learning machine (KELM), and to evaluate its effectiveness in differential diagnosis. Methods Totally 93 patients with 103 breast mass-like lesions confirmed by postoperative pathology or long-term follow-up underwent MRI. According to the breast imaging report and data system (BI-RADS) scoring guidelines, 12 MR imaging features and clinical features were selected. Then benign and malignant lesions were classified by one junior and one senior radiologist independently. The diagnostic efficacy was calculated. Results The sensitivity, specificity and accuracy of KELM in differential diagnosis of benign and malignant breast mass-like lesions were 0.88, 0.89, 0.91 and 0.93, 0.91, 0.92 for junior and senior doctor respectively, and AUC was 0.84 and 0.89. The sensitivity, specificity and accuracy of independent diagnosis of junior and senior doctor were 0.91, 0.74, 0.86 and 0.90, 0.85, 0.92, respectively, and AUC was 0.83 and 0.90, respectively. Conclusion KELM based on imaging features and clinical data can be used as asssitant in differential diagnosis of benign and malignant mass-like breast lesions, which has ideal sensitivity, specificity and accuracy.
