MRI Radiomics of Breast Cancer: Machine Learning-Based Prediction of Lymphovascular Invasion Status

KAYADİBİ Y., Kocak B., Ucar N., Akan Y. N., Yildirim E., Bektas S.

ACADEMIC RADIOLOGY, vol.29, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 29
  • Publication Date: 2022
  • Doi Number: 10.1016/j.acra.2021.10.026
  • Journal Name: ACADEMIC RADIOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CINAHL, EMBASE, MEDLINE
  • Keywords: Breast cancer, Lymphovascular invasion, Machine learning, Radiomics, Texture analysis, PREOPERATIVE PREDICTION, LOCOREGIONAL RECURRENCE, PROGNOSTIC-FACTORS, FEATURES, THERAPY, RADIOGENOMICS, HETEROGENEITY, COEFFICIENTS, CARCINOMA, NUMBER
  • Istanbul University Affiliated: Yes

Abstract

Rationale and Objectives: In patients with breast cancer (BC), lymphovascular invasion (LVI) status is considered an important prognostic factor. We aimed to develop machine learning (ML)-based radiomics models for the prediction of LVI status in patients with BC, using preoperative MRI images.