Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach.

Journal: Breast (Edinburgh, Scotland)
PMID: 39826386

Abstract

BACKGROUND: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-based tool to facilitate personalized treatment decisions.

Authors

  • Sakhr Alshwayyat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Mahmoud Bashar Abu Al Hawa
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan. Electronic address: mahmoud2000hawa@gmail.com.
  • Mustafa Alshwayyat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Tala Abdulsalam Alshwayyat
    Faculty of Medicine, Jordan University of Science & Technology, Irbid, Jordan.
  • Siya Sawan
    Faculty of Medicine, University of Jordan, Amman, Jordan. Electronic address: Siya.sawan.7@gmail.com.
  • Ghaith Heilat
    Breast Oncoplastic and General Surgery, Department of General Surgery and Urology, Jordan University of Science & Technology, King Abdullah University Hospital, Irbid, Jordan. Electronic address: gbheilat@just.edu.jo.
  • Hanan M Hammouri
    Department of Mathematics and Statistics, Faculty of Arts and Science, Jordan University of Science and Technology, Irbid, Jordan. Electronic address: hmhammouri@just.edu.jo.
  • Sara Mheid
    Radiation Oncology Department, King Hussein Cancer Center, Amman, Jordan. Electronic address: SM.11445@khcc.jo.
  • Batool Al Shweiat
    Breast Imaging Fellow, Department of Radiology, King Hussein Cancer Center, Amman, Jordan. Electronic address: Ba.17390@khcc.jo.
  • Hamdah Hanifa
    Faculty of Medicine, University of Kalamoon, Al-Nabk, Syria.

Keywords

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