A novel prediction method for lymph node involvement in endometrial cancer: machine learning

Gunakan E., Atan S., Haberal A. N., Kucukyildiz İ., Gokce E., Ayhan A.

INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER, cilt.29, sa.2, ss.320-324, 2019 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 2
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1136/ijgc-2018-000033
  • Dergi Adı: INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.320-324
  • Anahtar Kelimeler: endometrial cancer, lymph node involvement, lymph node status, machine learning, INTRAOPERATIVE FROZEN-SECTION, RISK, METASTASIS, LYMPHADENECTOMY, DIAGNOSIS
  • Sivas Cumhuriyet Üniversitesi Adresli: Hayır

Özet

Objective The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. In this study we constructed prediction models for EC patients using the Naive Bayes machine learning algorithm for LNI prediction.