Aims To evaluate the diagnostic significance of the novel index combining preoperative hemoglobin, albumin levels, lymphocyte and platelet counts (HALP) in prostate cancer (PCa) patients. Methods Between January 1, 2015 to December 31, 2018 at the Hitit University Erol Olçok Education and Research Hospital 225 patients who had undergone transrectal ultrasound guided prostate biopsy or prostate surgery were analyzed retrospectively. A total of patients; 155 had benign prostate hyperplasia (group 1) and 70 had PCa (group 2). The preoperative serum levels of hemoglobin, albumin, lymphocyte counts, and platelet counts were recorded. The HALP scores and the sub parameters of this index for each of the two groups were compared. Results The total Prostate Specific Antigen (PSA), albumin and lymphocyte differences between the groups were statistically significant (p=0.0002, p=0.0001, p= 0.005). The median value of HALP scores in Group 1 and 2 were 49.43 and 51.2 respectively, and this was not statistically significant between groups (p=0.737). The HALP score had the least Area Under Curve (AUC) value compared to the others (0.514). the AUC of Albumin was larger than PSA for diagnostic efficacy in PCa patients (0.696‐0.656). However, albumin levels were statistically significant compared to platelet count and the HALP score (p=0.0033, p=0.0068), except PSA and lymphocyte (p=0.4580, p=0.1717). Conclusion Further prospective clinical studies that include more patients from multiple centers are needed to show the diagnostic role of the HALP score and its compounds on the patients with PCa.

Urinary retention is defined as the inability to void; frequently the patient is able to void partially, thus can not completely empty the bladder and diagnosed by increased post-void residual urine (PVR) volumes. It is usually seen in men, but a rare condition in women. The etiological factors may be neurological, anatomical, infectious or pharmacological. It can be encountered after gynecological or incontinence surgery. Urethral calculi associated with acute urinary retention in women is a very rare condition, because of the anatomic structure of female urethra. In this study, a case of acute urinary retention due to the urethral calculus in a middle-aged woman was presented.

To find the machine learning (ML) method that has the highest accuracy in predicting the semen quality of men based on basic questionnaire data about lifestyle behavior. The medical records of men whose semen was analyzed for any reason were collected. Those who had data about their lifestyle behaviors were included in the study. All semen analyses of the men included were evaluated according to the WHO 2021 guideline. All semen analyses were categorized as normozoospermia, oligozoospermia, teratozoospermia, and asthenozoospermia. The Extra Trees Classifier, Average (AVG) Blender, Light Gradient Boosting Machine (LGBM) Classifier, eXtreme Gradient Boosting (XGB) Classifier, Logistic Regression, and Random Forest Classifier techniques were used as ML algorithms. Seven hundred thirty-four men who met the inclusion criteria and had data about lifestyle behavior were included in the study. 356 men (48.5%) had abnormal semen results, 204 (27.7%) showed the presence of oligozoospermia, 193 (26.2%) asthenozoospermia, and 265 (36.1%) teratozoospermia according to the WHO 2021. The AVG Blender model had the highest accuracy and AUC for predicting normozoospermia and teratozoospermia. The Extra Trees Classifier and Random Forest Classifier models achieved the best performance for predicting oligozoospermia and asthenozoospermia, respectively. The ML models have the potential to predict semen quality based on lifestyles.

Background: To establish a machine learning (ML) model for predicting prostate biopsy outcomes using prostate-specific antigen (PSA) values, multiparametric magnetic resonance imaging (mpMRI) findings, and hematologic parameters. Methods: The medical records of the patients who had undergone a prostate biopsy were evaluated. Laboratory findings, mpMRI findings, and prostate biopsy results were collected. Patients with benign prostate pathology were classified as Group 1, and those with prostate cancer (PCa) were classified as Group 2. The following ML algorithms were used to create the ML model: ExtraTrees classifier, Light Gradient-Boosting Machine (LGBM) classifier, eXtreme Gradient Boosting (XGB) classifier, Logistic Regression, and Random Forest classifier. Results: A total of 244 male patients who met the inclusion criteria were included in this study. Among them, 171 (71.1%) were categorized in Group 1, and 73 (29.9%) in Group 2. The LGBM classifier model demonstrated the highest performance, achieving an accuracy rate of 81.6% and an AUC–ROC (area under the curve–receiver operating characteristic) of 78.4%, with sensitivity and specificity values of 66.7% and 88.2%, respectively, in predicting prostate biopsy outcomes. Conclusions: Pathological results can be predicted by ML models using PSA values, mpMRI findings, and hematologic parameters prior to a prostate biopsy, potentially reducing unnecessary biopsy procedures.

Introduction: Prostate cancer is the most common malignancy among men in the United States and the second most common cancer in Turkey. The incidence of prostate cancer is increasing in industrialized countries. Aim: The aim of the study was to assess the knowledge about prostate cancer, its diagnosis, and treatment among patients with lower urinary tract symptoms. Methods: This study was performed from January to April 2015 with the patients applied to our clinic. A questionnaire that includes 10 questions was administered to the participants. Results: One hundred fifty-nine participants were included in this study. The participants’ ages were between 40 and 82 with a mean age of 61.5 ± 7.9 years. Patient awareness of prostate biopsy and prostate cancer were 21.37 and 71.06%. The main origin awareness of PSA testing is family and friends. On the other hand, if the doctor advises acout prostate biopsy, 47.16% of the patients would accept and 11.31% of them would refuse this invasive procedure. Conclusion: Prostate cancer is one of the important health-related problem among men in the world. Additional researches are needed to investigate the knowledge of prostate cancer among men and the Ministry of Health may take preventive methods to increase the cancer knowledge level of people.

This study aimed to systematically compare the most recent guidelines from the European Association of Urology (EAU, 2025), American Urological Association