The preoperative albumin level is an independent prognostic factor for overall survival in optimally debulked EOC patients. Further investigations about preoperative albumin level in prognostic models will contribute to the literature.
Objective Epithelial ovarian cancer (EOC) requires an aggressive surgical approach. The important part of literature on ovarian cancer surgery emphasize residual tumor and survival analyses. Morbidity issue keeps in background. Therefore, we aimed to report on morbidity of cytoreductive surgery for EOC in this study. Methods EOC patients who underwent primary debulking were evaluated. Intraoperative and postoperative complications that occurred within 30 days after the surgery and factors that affect morbidity were considered. Results The study involved 359 patients. Forty-six intraoperative complications occurred in 42 (11.6%) patients. Advanced stage and cancer antigen level of 125 were independently and significantly associated with operative complications (hazard ratio [HR], 1.66; 95% confidence interval [CI], 1.01-2,73; P=0.044, and HR, 1.47; 95% CI, 1.05-2.06; P=0.025, respectively). The need for intensive care unit admission was significantly higher in patients with intraoperative complications (28.6% vs. 8.8%, P=0.001). Intraoperative and postoperative complication rates were significantly higher in extended surgery than in standard surgery (18.9%vs. 8.5%, P=0.005 and 38.7% vs. 10.9%, P<0.001, respectively). Intraoperative and postoperative transfusion need, hospital stay duration, and chemotherapy start day were also significantly higher in extended surgery than in standard surgery. Hundred postoperative complications occurred in 70 patients. Age, extended surgery, presence of ascites, and presence of operative complications were independently and significantly associated with postoperative complications. Conclusion Morbidity of extensive surgical approach should be kept in mind in ovarian cancer surgery aimed at leaving no residual tumor. Patient-based management with an appropriate preoperative evaluation may avoid morbidity of extended/extensive surgical approaches.
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.
The incidence of premalignant and malignant endometrial disorders increases during the postmenopausal period. In the literature, endometrial disorders are usually discussed in the context of menopausal status. But there are limited data regarding endometrial disorders in geriatric patients. Early diagnosis of endometrial cancers with aggressive behaviour that increases during the geriatric period may allow simpler treatment options and also decrease the treatment-associated morbidity risk.Records of geriatric patients who underwent an endometrial histopathological evaluation between 2011 and 2016 were evaluated. Clinical findings, transvaginal ultrasonography findings, endometrial sampling methods, and histopathological results were evaluated.A total of 188 patients were included in the study (mean age 70.3 ±5.6 years). The most common histopathological results were endometrial polyp, atrophic endometrium, and surface epithelium (26.6%, 22.3%, and 12.8%, respectively). None of the 57 patients without vaginal bleeding had endometrial cancer. In 131 patients with vaginal bleeding, mean endometrial thickness was 9.8 ±8.1 mm (2-49 mm) and the rate of endometrial disorders was 56.5% (74 patients). Endometrial cancer was diagnosed in 19 patients (10.1%), and 36.8% of them had non-endometrioid cancers. The presence of vaginal bleeding was significantly associated with the diagnosis of endometrial cancer and any endometrial disorder (p = 0.001 and p = 0.000, respectively).The incidence of non-endometrioid endometrial cancers increased in the geriatric period. An endometrial histopathological examination should be considered, especially for patients with a history of vaginal bleeding. Further investigation of the endometrial thickness cut-off levels in the geriatric period will contribute to the literature.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.