Browsing by Author "Kucukyildiz, Irem Alyazici"

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Does Polyp-Originated Growing have Prognostic Significance for Stage 1 Endometrioid-Type Endometrial Cancer?
(2020) Kucukyildiz, Irem Alyazici; Gunakan, Emre; Akilli, Huseyin; Haberal, Asuman Nihan; Kuscu, Esra; Haberal, Ali; Ayhan, Ali; 0000-0002-5240-8441; 0000-0002-0992-6980; 0000-0001-9852-9911; 0000-0002-1486-7209; AAX-3230-2020; AAI-8792-2021; AAK-4587-2021; AAI-9331-2021
Purpose Endometrioid-type endometrial cancer is usually diagnosed in the early stages and has a good prognosis. Patients with stage 1 disease have survival rates over 95%. Tumor factors affect survival in these patients, but polyp-originated growing has not been sufficiently discussed in the literature. This study aimed to determine the effect of polyp-originated growing in stage 1 endometrioid-type endometrial cancer and to provide a review of the literature. Methods This study includes 318 stage 1 endometrioid-type endometrial cancer patients. The patients were divided into two groups based on the tumor origin. Group I included patients with polyp-originated growing tumors, and Group II included patients with endometrial surface-originated growing tumors. Results Groups I and II included 39 and 279 patients, respectively. The general properties of the patients were similar; there were no significant differences. The univariate survival analyses showed that overall survival for Groups I and II was 65.5 and 83.6 months, respectively; this difference was statistically significant (p = 0.002). The multivariate analysis of age, maximum tumor diameter, tumor origin, lymphovascular space involvement, myometrial invasion depth and tumor grade showed that polyp-originated growing was independently and significantly associated with overall survival (HR 4.05; 95% CI 1.2-13.5; p = 0.023). Conclusion Polyp-originated growing may be a prognostic factor for early stage endometrioid-type endometrial cancer. The prognostic effect of polyp-originated growing is not well known, and further investigation is necessary.
A novel prediction method for lymph node involvement in endometrial cancer: machine learning
(2019) Gunakan, Emre; Atan, Suat; Haberal, Asuman Nihan; Kucukyildiz, Irem Alyazici; Gokce, Ehad; Ayhan, Ali; 30718313
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. 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.