Computational intelligence methods, including non-linear classification algorithms, can be used in medical research and practice as a decision making tool. This study aimed to evaluate the usefulness of artificial intelligence models for 5–year overall survival prediction in patients with cervical cancer treated by radical hysterectomy. The data set was collected from 102 patients with cervical cancer FIGO stage IA2-IIB, that underwent primary surgical treatment. Twenty-three demographic, tumor-related parameters and selected perioperative data of each patient were collected. The simulations involved six computational intelligence methods: the probabilistic neural network (PNN), multilayer perceptron network, gene expression programming classifier, support vector machines algorithm, radial basis function neural network and k-Means algorithm. The prediction ability of the models was determined based on the accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve. The results of the computational intelligence methods were compared with the results of linear regression analysis as a reference model. The best results were obtained by the PNN model. This neural network provided very high prediction ability with an accuracy of 0.892 and sensitivity of 0.975. The area under the receiver operating characteristics curve of PNN was also high, 0.818. The outcomes obtained by other classifiers were markedly worse. The PNN model is an effective tool for predicting 5–year overall survival in cervical cancer patients treated with radical hysterectomy.

The aim of this article was to compare gene expression programming (GEP) method with three types of neural networks in the prediction of adverse events of radical hysterectomy in cervical cancer patients. One-hundred and seven patients treated by radical hysterectomy were analyzed. Each record representing a single patient consisted of 10 parameters. The occurrence and lack of perioperative complications imposed a two-class classification problem. In the simulations, GEP algorithm was compared to a multilayer perceptron (MLP), a radial basis function network neural, and a probabilistic neural network. The generalization ability of the models was assessed on the basis of their accuracy, the sensitivity, the specificity, and the area under the receiver operating characteristic curve (AUROC). The GEP classifier provided best results in the prediction of the adverse events with the accuracy of 71.96 %. Comparable but slightly worse outcomes were obtained using MLP, i.e., 71.87 %. For each of measured indices: accuracy, sensitivity, specificity, and the AUROC, the standard deviation was the smallest for the models generated by GEP classifier.

Objective: We assessed the prognostic factors related to 10-year overall survival and disease-free survival in cervical cancer patients that underwent primary surgical protocols in 1 institution. Materials and Methods: A total of 102 patients with uterine cervical cancer at FIGO stages IA2-IIB that underwent a Piver type III radical hysterectomy and pelvic lymphadenectomy between 1998 and 2001 were included. Univariate and multivariate analyses of 10-year overall survival and 10-year disease-free survival were performed. Results: Univariate analysis revealed that only lymphovascular space invasion significantly affected 10-year overall survival (p = 0.04), but it had no effect on the 10-year disease-free survival rate. Multivariate analysis demonstrated that survival rates were significantly affected by FIGO stage (p = 0.02, 95% CI: 1.18-5.55, for 10-year overall survival; p = 0.03, 95% CI: 1.07-6.12, for 10-year disease-free survival) and metastases to the pelvic lymph nodes (p = 0.0005, 95% CI: 1.81-8.53, for 10-year overall survival; p = 0.01, 95% CI: 1.26-7.24, for 10-year disease-free survival). Conclusions: The only independent prognostic factors for 10-year survival rates in patients with cervical cancer at FIGO stages IA2-IIB were clinical stage and presence of metastases to the pelvic lymph nodes. The presence of lymphovascular space invasion adversely affected 10-year overall survival.

The World Health Organization (WHO) determines infertility as a disease of the reproductive system defined clinically by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse. Estimates indicate that the problem of infertility in the world is continuing to grow. The aim of the study was to compare approaches to disease in partners of both sexes diagnosed with infertility. The study was conducted among 61 couples treated for infertility using an original questionnaire developed by the authors. The Chi square independence test was used for statistical analysis. Both men and women responded to the diagnosis of infertility with negative emotions. Regardless of sex, sadness and anxiety were the dominant feelings associated with the diagnosis of infertility. Women believed in the success of the treatment to a greater extent than men. Mainly women attempted to talk openly about the problem of infertility, while men were more restrained in this respect. Women accepted the assisted reproductive technologies (ART) to a greater extent than men, but men would accept childlessness more often than women.

The term personalized medicine was created for oncological patients, but due to its positive clinical results it is now used in many other fields of medicine, including reproductive medicine. The aim of the study was to determine the level of stress and strategies of coping with stress in patients treated for infertility. The study—using a questionnaire developed by the authors, the Perceived Stress Scale-10 (PSS-10), and the Coping Orientation to Problems Experienced Inventory (Mini-COPE)—was conducted among 456 people from infertile couples. Conclusions: More than half of the studied patients demonstrated a high level of stress. The choice of coping strategies was related to the respondents’ gender and level of stress as well as their experience with assisted reproductive technology.

Objectives: To evaluate magnetic resonance elastography as a tool for characterizing uterine leimyomas. Material and methods: At total of 12 women with symptomatic leiomyomas diagnosed in physical and ultrasound examinations were enrolled in this pilot study. Before surgery, all patients underwent magnetic resonance elastography of the uterus using a 1.5 T MR whole-body scanner (Optima, GE Healthcare, Milwaukee, WI, USA). Surgical specimens were forwarded for histological examination. The findings were allocated into 3 categories depending on the percentage content of connective tissue: below 15%, from 15 to 30% and more than 30%. The median stiffness of leiomyomas for each of the group was calculated. The U-Mann Whitney test was used for statistical analysis. Results: The stiffness of the leiomyomas ranged between 3.7–6.9 kPa (median value 4.9 kPa). The concentration of extracellular components in the leiomyomas did not exceed 40%. An increasing trend of the stiffness with the growing percentage of extracellular component was observed. Stiffness of the leiomyomas obtained by MRE varies depending on microscopic composition. Conclusions: The value of stiffness shows a trend of increasing with the percentage of extracellular component of the leiomyoma. Further studies are required to assess the usefulness of MRE in diagnostics of uterine leiomyomas. Objectives: To evaluate magnetic resonance elastography as a tool for characterizing uterine leimyomas. Material and methods: At total of 12 women with symptomatic leiomyomas diagnosed in physical and ultrasound examinations were enrolled in this pilot study. Before surgery, all patients underwent magnetic resonance elastography of the uterus using a 1.5 T MR whole-body scanner (Optima, GE Healthcare, Milwaukee, WI, USA). Surgical specimens were forwarded for histological examination. The findings were allocated into 3 categories depending on the percentage content of connective tissue: below 15%, from 15 to 30% and more than 30%. The median stiffness of leiomyomas for each of the group was calculated. The U-Mann Whitney test was used for statistical analysis. Results: The stiffness of the leiomyomas ranged between 3.7–6.9 kPa (median value 4.9 kPa). The concentration of extracellular components in the leiomyomas did not exceed 40%. An increasing trend of the stiffness with the growing percentage of extracellular component was observed. Stiffness of the leiomyomas obtained by MRE varies depending on microscopic composition. Conclusions: The value of stiffness shows a trend of increasing with the percentage of extracellular component of the leiomyoma. Further studies are required to assess the usefulness of MRE in diagnostics of uterine leiomyomas.