A Radiomics Approach for the Classification of Fibroepithelial Lesions on Breast Ultrasonography

Yongsik Sim, Si Eun Lee, Eun Kyung Kim, Sungwon Kim

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A radiomics-based classifier to distinguish phyllodes tumor and fibroadenoma on gray-scale breast ultrasonography was developed and validated. A total of 93 radiomics features were extracted from representative transverse plane ultrasound images of 182 fibroepithelial lesions initially diagnosed by core needle biopsy. High-throughput radiomics features were selected using the intra-class correlation coefficient between two radiologist readers and the Least Absolute Shrinkage and Selection Operator regression through 10-fold cross-validation. When applied to the validation set, the radiomics classifier for the differentiation of phyllodes tumors and benign/fibroadenomas achieved an area under the receiver operating characteristic curve of 0.765 (95% confidence interval [CI]: 0.597–0.888) with an accuracy of 0.703 (sensitivity: 0.857; specificity: 0.5). Our radiomics signature-based classifier may help predict phyllodes tumors among fibroepithelial lesions on breast ultrasonography.

Original languageEnglish
Pages (from-to)1133-1141
Number of pages9
JournalUltrasound in Medicine and Biology
Volume46
Issue number5
DOIs
Publication statusPublished - 2020 May

All Science Journal Classification (ASJC) codes

  • Biophysics
  • Radiological and Ultrasound Technology
  • Acoustics and Ultrasonics

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