Evaluation of acute hematological toxicity by machine learning in gynecologic cancers using postoperative radiotherapy.

Yakar M., Etiz D., Celik Ö., Ozen A.

Indian journal of cancer, cilt.59, sa.2, ss.178-186, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 59 Sayı: 2
  • Basım Tarihi: 2022
  • Doi Numarası: 10.4103/ijc.ijc_666_19
  • Dergi Adı: Indian journal of cancer
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, EMBASE, MEDLINE, Veterinary Science Database
  • Sayfa Sayıları: ss.178-186
  • Anahtar Kelimeler: Cancer, machine learning, radiotherapy, toxicity, INTENSITY-MODULATED RADIOTHERAPY, BONE-MARROW, RADIATION-THERAPY, CERVICAL-CANCER, MALIGNANCIES, PREDICTORS, VOLUME, IMRT, ARC
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Background: The aim of the study is to investigate the factors affecting acute hematologic toxicity (HT) in the adjuvant radiotherapy (RT) of gynecologic cancers by machine learning.