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

Yakar M. Ç., Etiz D., Celik Ö., Ozen A.

Indian journal of cancer, vol.59, no.2, pp.178-186, 2022 (SCI-Expanded, Scopus) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 59 Issue: 2
  • Publication Date: 2022
  • Doi Number: 10.4103/ijc.ijc_666_19
  • Journal Name: Indian journal of cancer
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, CINAHL, EMBASE, MEDLINE, Veterinary Science Database
  • Page Numbers: pp.178-186
  • Keywords: Cancer, machine learning, radiotherapy, toxicity, INTENSITY-MODULATED RADIOTHERAPY, BONE-MARROW, RADIATION-THERAPY, CERVICAL-CANCER, MALIGNANCIES, PREDICTORS, VOLUME, IMRT, ARC
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

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.