Prediction of Survival and Recurrence Patterns by Machine Learning in Gastric Cancer Cases Undergoing Radiation Therapy and Chemotherapy

AKÇAY, MELEK; ETİZ, DURMUŞ; çelik, ÖZER

doi:10.1016/j.adro.2020.07.007

Prediction of Survival and Recurrence Patterns by Machine Learning in Gastric Cancer Cases Undergoing Radiation Therapy and Chemotherapy

AKÇAY M. Ç., ETİZ D., çelik Ö.

ADVANCES IN RADIATION ONCOLOGY, cilt.5, sa.6, ss.1179-1187, 2020 (ESCI, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 5 Sayı: 6
Basım Tarihi: 2020
Doi Numarası: 10.1016/j.adro.2020.07.007
Dergi Adı: ADVANCES IN RADIATION ONCOLOGY
Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus
Sayfa Sayıları: ss.1179-1187
Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Purpose: Radical surgery is the most important treatment modality in gastric cancer. Preoperative or postoperative radiation therapy (RT) and perioperative chemotherapy are the treatment options that should be added to surgery. This study aimed to evaluate the overall survival (OS) and recurrence patterns by machine learning in gastric cancer cases undergoing RT.