Relationship between contraceptive methods and human papillomavirus positivity.

PURPOSE: Understanding the effect of contraceptive use on high-risk human papillomavirus (HPV) positivity may provide information that is valuable to women in contraceptive decision-making. This study includes women aged 30-65 years who admitted to Family Planing outpatient clinic and have hrHPVDNA positivity. METHODS: We included a total of 801 women. All participants underwent national cervical cancer screening using HPV screening test conducted by the Cancer Control Department of the Ministry of Health. They completed a questionnaire on demographic information and potential risk factors. RESULTS: The HPV DNA positivity rate among all participants was 8.4%. The two most common HPV genotypes were HPV16 and HPV51. Meanwhile, hrHPV infection was associated with age, marital status, smoking status, and contraceptive method. CONCLUSION: HPV is the most common cause of sexually transmitted diseases. Understanding about the reproductive and demographic characteristics affecting HPV persistence is crucial. The effect of contraceptive methods on HPV positivity is important information that is necessary to be relayed to women by healthcare professionals.

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

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.