Prediction of malignancy upgrade rate in high-risk breast lesions using an artificial intelligence model: a retrospective study.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date:

Abstract

PURPOSE: High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy.

Authors

  • Özge Aslan
    Department of Radiology, Ege University Faculty of Medicine, İzmir, Turkey
  • Ayşenur Oktay
    Department of Radiology, Ege University Faculty of Medicine, İzmir, Turkey
  • Başak Katuk
    Department of Computer Engineering, Ege University, İzmir, Turkey
  • Riza Cenk Erdur
    Department of Computer Engineering, Ege University, İzmir, Turkey
  • Oğuz Dikenelli
    Department of Computer Engineering, Ege University, İzmir, Turkey
  • Levent Yeniay
    Department of General Surgery, Ege University Faculty of Medicine, İzmir, Turkey
  • Osman Zekioğlu
    Department of Medical Pathology, Ege University Faculty of Medicine, İzmir, Turkey
  • Süha Süreyya Özbek
    Department of Radiology, Ege University Faculty of Medicine, İzmir, Turkey