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

• Published in: International journal of gynecological cancer Vol. 29; no. 2; pp. 320 - 324
• Main Authors: Günakan, Emre, Atan, Suat, Haberal, Asuman Nihan, Küçükyıldız, İrem Alyazıcı, Gökçe, Ehad, Ayhan, Ali
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2019; by the International Gynecologic Cancer Society and the European Society of Gynaecological Oncology; Elsevier Limited
• Subjects: Accuracy; Adult; Aged; Aged, 80 and over; Algorithms; Datasets; Endometrial cancer; Endometrial Neoplasms - pathology; Endometrial Neoplasms - surgery; Female; Follow-Up Studies; Gynecology; Histology; Humans; Lymph Node Excision; lymph node involvement; lymph node status; Lymph Nodes - pathology; Lymph Nodes - surgery; Lymphatic system; Machine Learning; Middle Aged; Models, Statistical; Multivariate analysis; Ovaries; Patients; Predictive Value of Tests; Retrospective Studies; Statistical analysis; Surgery; endometrial cancer; lymph node status; machine learning; lymph node involvement
• ISSN: 1048-891X; 1525-1438; 1525-1438
• DOI: 10.1136/ijgc-2018-000033

ObjectiveThe 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.MethodsThe 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.ResultsThe 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).ConclusionsMachine 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.