Prediction of endometrial cancer recurrence by using a novel machine learning algorithm: An Israeli gynecologic oncology group study

Ohad Houri, Yotam Gil, Ofer Gemer, Limor Helpman, Zvi Vaknin, Ofer Lavie, Alon Ben Arie, Amnon Amit, Tally Levy, Ahmet Namazov, Inbar Ben Shachar, Ilan Atlas, Ilan Bruchim, Ram Eitan

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Objectives: Endometrial cancer is the most common gynecologic malignancy in developed countries. The overall risk of recurrence is associated with traditional risk factors. Methods: Machine learning was used to predict recurrence among women who were diagnosed and treated for endometrial cancer between 2002 and 2012 at elven university-affiliated centers. The median follow-up time was 5 years. The following data were retrieved from the medical records and fed into the algorithm: age, chronic metabolic diseases, family and personal cancer history, hormone replacement therapy use, endometrial thickness, uterine polyp presence, complete blood count results, albumin, Ca-125 level, surgical staging, histology, depth of myometrial invasion, LVSI, grade, pelvic washing cytology, and adjuvant treatment. We used XGBoost algorithm, which fits the training data using decision trees, and can also rate the factors according to their influence on the prediction. Results: 1935 women were identified of whom 325 had recurrent disease. On the randomly picked samples, the specificity was 55% and the sensitivity was 98%. Our model showed an operating characteristic curve with AUC of 0.84. Conclusions: A machine learning algorithm presented promising ability to predict recurrence of endometrial cancer. The algorithm provides an opportunity to identify at-risk patients who may benefit from adjuvant therapy, tighter surveillance, and early intervention.

Original languageEnglish
Article number102466
JournalJournal of Gynecology Obstetrics and Human Reproduction
Issue number9
StatePublished - Nov 2022

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  • Artificial intelligence
  • Endometrial carcinoma
  • Machine learning
  • Personalized medicine
  • Prediction


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