Anthropometric characteristics and relationship with non-muscle invasive bladder cancer in Greece: A case-control study.

BACKGROUND: Risk factors like smoking, radiation, chronic infections and exposure to occupational chemicals are strongly associated with occurrence of bladder cancer. Association between increased body weight and bladder cancer has been controversial. The aim of this case-control study is to evaluate association of anthropometric characteristics on bladder cancer incidence in Greek population. METHODS: This case-control study was conducted at a tertiary hospital in Greece with cases being patients with bladder cancer diagnosed within the last 2 years and controls patients admitted to hospital for reason other than cancer and not related to common risk factors related to bladder cancer. Anthropometric characteristics like weight, height, body mass index, waist and hip circumference were measured. Analyses was done with R (Vienna, Austria). RESULTS: Comparison between groups showed that patients with bladder cancer had higher weight, BMI and waist circumference compared to controls. However, multivariate, binomial logistic regression showed that only age (OR 1.03, 95% CI: 1-1.05, p = 0.02), no use of smoke (OR 0.12, 95% CI: 0.07-0.23, p < 0.001) and occupation related to bladder cancer (OR 7.45, 95% CI: 2.53-27.93, p < 0.001) significantly predicted the incidence of bladder cancer. CONCLUSIONS: Bladder cancer incidence is strongly linked with specific risk factors such as smoking, occupation with exposure to chemicals and smoke, increasing age, radiation and chronic infections. Several studies have shown a weak association between anthropometric characteristics and bladder cancer, although most studies in European populations did not confirm these findings. Similarly in our case-control study in a Greek population, we found potential relationship between increased weight/BMI and waist circumference with bladder cancer, but the association disappeared in multivariate analysis.

Acceptance of Artificial Intelligence in Supporting Cancer Patients.

ASCAPE Project is a study aiming to implement the advances of Artificial Intelligence (AI), to support prostate cancer survivors, regarding quality of life issues. The aim of the study is to determine characteristics of patients who accepted to join ASCAPE project. It results that participants of the study mainly originate from higher-educated societies that are better informed about the potential benefits of AI in medicine. Therefore, efforts should be focused on eliminating patients' reluctancy by better informing them on the potential benefits of AI.

Training ChatGPT Models in Assisting Urologists in Daily Practice.

Artificial Intelligence (AI) has shown the ability to enhance the accuracy and efficiency of physicians. ChatGPT is an AI chatbot that can interact with humans through text, over the internet. It is trained with machine learning algorithms, using large datasets. In this study, we compare the performance of using a ChatGPT API 3.5 Turbo model to a general model, in assisting urologists in obtaining accurate, valid medical information. The API was accessed through a Python script that was applied specifically for this study based on 2023 EAU guidelines in PDF format. This custom-trained model leads to providing doctors with more precise, prompt answers about specific urologic subjects, thus helping them, ultimately, providing better patient care.

Using Association Rules in Antimicrobial Resistance in Stone Disease Patients.

Association rule mining is a very popular unsupervised machine learning technique for discovering patterns in large datasets. Patients with stone disease commonly suffer from urinary tract infections (UTI), complicated by the emergence of antimicrobial resistance (AMR), due to the excessive use of antibiotics. In this study, we explore the use of association rule mining in the AMR profile of patients suffering from stone disease.

Cluster Analysis Assessment in Proposing a Surgical Technique for Benign Prostatic Enlargement.

Benign prostatic enlargement (BPE) is a common disease in men over 50 years old. The phenotype of patients with BPE is heterogenous, regarding both baseline patient characteristics and disease-related parameters. Treatment can be either medical-conservative or surgical. A great variety of surgical techniques are available for surgical management, with three of the most common being monopolar transurethral resection of the prostate (mTUR-P), bipolar transurethral resection of the prostate (bTUR-P), and bipolar transurethral vaporization of the prostate (bTUVis). The selection of each one of these depends on surgeon reasoning, equipment availability, patient characteristics, and preferences. Since all of these techniques are available in our Urology Department, and surgeons are skilled to perform each one of them, we performed a clustering analysis according to patient pre-operative characteristics, using the k-means algorithm, to compare clustering-related technique assignment with the real-life technique used.