View clinical trials related to Prostatic Neoplasms.
Filter by:This study aimed to investigate the effects of prostate cancer on patients' physical activity, kinesiophobia, fatigue and functionality. This research is a prospective study to be conducted on volunteer individuals between the ages of 40-75. People diagnosed with prostate cancer (study group) and healthy adults who have not been diagnosed with prostate cancer before (control group) will be included in the study. The demographic characteristics, physical activity levels and quality of life of all individuals participating in the study will be evaluated with an online form. In demographic data, physical, sociodemographic data such as age (years), height (cm), body weight (kg), body mass index (kg/m2) and disease-specific information will be recorded. Physical activity level will be measured with the International Physical Activity Survey short form (UFAA), fatigue with the Functional Evaluation of Chronic Disease Treatment-Fatigue Questionnaire, fear of movement with the Causes of Fear of Movement Questionnaire, and quality of life with the Functional Evaluation of Cancer Treatment-Prostate Version questionnaire (KHTFD-Y).
The investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier.
MR prostate exam is essential for the diagnosis, workup and follow-up of prostate cancer. It allows to detect subclinical prostate cancer following an increase in the level of PSA. The investigators can score the lesion according to the PIRADS classification and obtain an estimate of lesion malignancy. To perform this classification, T2 and DWI sequences are essential. Detection and characterization of malignant lesion is important to address appropriate patient care pathway. The purpose of this project is to evaluate novel deep learning (DL) T2-weighted TSE (T2DL) and Diffusion (DWIDL) sequences for prostate MR exam and investigate its impact on diagnostic, examination time, image quality, and PI-RADS classification compared to standard T2-weighted TSE (T2S) and standard Diffusion (DWIS) sequences.
The generation of predictive models in radiotherapy has seen a significant increase. In 2017, Raymond published the largest systematic review of predictive prognostic models for biochemical relapse (BR), metastasis-free survival, and overall survival in patients with localized prostate cancer treated with radiotherapy (14), attempting to identify whether they were adequately developed and validated. He found 72 unique predictive models for external radiotherapy: 22 corresponding to BR risk, 20 corresponding to Cancer-Specific Survival, 10 corresponding to Overall Survival, and 20 for Disease/Metastasis-Free Survival detection. In his analysis, he highlighted a significant variation in the quality of these predictive models, understanding that they were developed prior to the existence of TRIPOD guidelines. In this regard, he pointed out that 54% of these models did not report their accuracy, and 61% of the models lacked validation (either internal or external). He also noted that they had limited follow-up (only 65% had follow-up beyond 5 years), that the treatment doses in these models were lower than current standards, and that the radiation techniques were different from current practices. Although in his final assessment, Raymond maintains that predictive models provide more certainty in predicting oncological outcomes than professional assessments, he considers it vital to validate these models for each population that wants to use them (the vast majority of these models are based on U.S. populations) or, even better, to generate predictive models specific to the local population while adhering to the TRIPOD guidelines. Probably due to the lack of validation in our patients for existing predictive models and/or the absence of predictive models originating from our population, in our routine clinical practice (Multidisciplinary Oncology Committees), phisycians do not apply any predictive models to patients diagnosed with localized prostate cancer.
Recent guidelines now recommend multi parametric magnetic resonance imaging prior to biopsy for all men as an integral part of improved diagnosis of clinical significant prostate cancer. However, magnetic resonance imaging targeted biopsy is a strategy that focuses on maximizing detection of clinical significant prostate cancer, but this procedure has the disadvantage of leading to higher detection of clinically insignificant prostate cancers. One of the risk-stratifications developed to minimize the existing disadvantages and avoid unnecessary biopsy procedures is a strategy in which multi parametric magnetic resonance imaging and prostate-specific antigen density are used in combination. This is especially important in all patients with PI-RADS (Prostate Imaging Reporting and Data System) 3 lesions which are also interpreted as indeterminate mpMRI findings. Current guidelines suggest that biopsy may be omitted in some patient groups with PI-RADS 3 lesions in the risk-adapted strategy involving prostate-specific antigen density. The aim of this study was to evaluate the role of risk-adapted strategies involving prostate-specific antigen density in biopsy decision to avoid unnecessary biopsy vs the risk of missing clinical significant prostate cancer diagnosis in patients with PI-RADS 3 lesions.
Background: Prostate biopsies are essential to diagnose prostate cancer (PCa). Transrectal prostate biopsies (TR-PB) are commonly performed, however disadvantages include the requirement of antibiotic prophylaxis (AP) and higher complication rates than transperineal prostate biopsies (TP-PB). Guidelines still recommend the use of AP for TP-PB due to the limited evidence regarding complication rates after their omission. However, the rising rates of antibiotic resistance is of concern. The aim of this study was to compare the complication and detection rates of freehand TP-PB without AP versus TR-PB with AP. Methods: This single center retrospective study was performed in an academic hospital. TP-PB were introduced in 2019 and implemented as the main technique by late 2020. To compare the two techniques, data was collected for freehand TR-PB with AP between 2017-2018 and freehand TP-PB without AP between 2021-2022. The data from 2019 and 2020 were excluded to rule out the effects of the initial learning curve during the transition period. Primary outcome measure was post-biopsy complications occurring within 2 weeks, focusing on infectious complications. Secondary outcome measures were detection rates and upgrading/reclassification in the repeat biopsy in active surveillance (AS). Statistical analysis was performed using a Fisher exact or Chi-Squared test.
The goal of this observational study was to compare the perioperative outcomes, postoperative urinary control rates and positive surgical margin (PSM) rates of the robot-assisted laparoscopic radical prostatectomy combined anterior and posterior approach (AP-RARP) with the Retzius-sparing approach (RS-RARP) and anterior approach (anterior-RARP) in the treatment of prostate cancer. The main question it aims to answer was: • The early therapeutic efficacy of the robot-assisted laparoscopic radical prostatectomy combined anterior and posterior approach Participants has been underwent: - AP-RARP - RS-RARP - anterior-RARP Researchers compared the three groups to see if AP-RARP combines the advantages of anterior and posterior RARP and is a feasible surgical option for the treatment of prostate cancer.
Study Design : A randomized, open-label, four-sequence, four-period, crossover, single dosing, phase 1 study
To optimize precision for secondary resection (SR) in frozen section (FS) controlled nerve-sparing robot-assisted radical prostatectomy (NS-RARP) by using a personalized 3D-printed prostate model.
The study team will evaluate the impact of video characteristics on health consumers' trust in online videos. Participants will be randomized to watch a video by one of four speakers about prostate cancer screening or clinical trials and complete a questionnaire (approximately 15 minutes total).