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A novel prediction method for lymph node involvement in endometrial cancer: machine learning
  1. Emre Günakan1,
  2. Suat Atan2,
  3. Asuman Nihan Haberal3,
  4. İrem Alyazıcı Küçükyıldız4,
  5. Ehad Gökçe4 and
  6. Ali Ayhan4
  1. 1 Department of Obstetrics and Gynecology, University of Medical Sciences, Keçioren Training and Research Hospital, Ankara, Turkey
  2. 2 Software Developer, Ankara, Turkey
  3. 3 Department of Pathology, Başkent University, School of Medicine, Ankara, Turkey
  4. 4 Department of Obstetrics and Gynecology, Başkent University, School of Medicine, Ankara, Turkey
  1. Correspondence to Emre Günakan, Department of Obstetrics and Gynecology, University of Medical Sciences , Keçioren Training and Research Hospital, Ankara 06380, Turkey; emreg43{at}


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 Naïve Bayes machine learning algorithm for LNI prediction.

Methods The study assessed 762 patients with EC. Algorithm models were based on the following histopathological factors: V1: final histology; V2: presence of lymphovascular space invasion (LVSI); V3: grade; V4: tumor diameter; V5: depth of myometrial invasion (MI); V6: cervical glandular stromal invasion (CGSI); V7: tubal or ovarian involvement; and V8: pelvic LNI. Logistic regression analysis was also used to evaluate the independent factors affecting LNI.

Results The mean age of patients was 59.1 years. LNI was detected in 102 (13.4%) patients. Para-aortic LNI (PaLNI) was detected in 54 (7.1%) patients, of which four patients had isolated PaLNI. The accuracy rate of the algorithm models was found to be between 84.2% and 88.9% and 85.0% and 97.6% for LNI and PaLNI, respectively. In multivariate analysis, the histologic type, LVSI, depth of MI, and CGSI were independently and significantly associated with LNI (p<0.001 for all).

Conclusions Machine learning may have a place in the decision tree for the management of EC. This is a preliminary report about the use of a new statistical technique. Larger studies with the addition of sentinel lymph node status, laboratory findings, or imaging results with machine learning algorithms may herald a new era in the management of EC.

  • endometrial cancer
  • lymph node involvement
  • lymph node status
  • machine learning

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  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Ethics approval The was a retrospective study so ethics approval not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.