View clinical trials related to Prostatic Neoplasms.
Filter by:Prostate cancer is the second most common cancer in the male population. This pathology represents an oncological and public health problem especially in developed countries, due to a greater presence of elderly men in the population. Medical imaging plays a central role in the staging and restaging of prostate disease. Magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are among the methods commonly used in normal clinical practice for the characterization of prostate cancer. To date, the study of these images is limited to a qualitative visual analysis, however there is increasing evidence relating to the usefulness of introducing a quantitative (or semi-quantitative) analysis of biomedical images. The current increase in available imaging data, and their quality, allows the application of artificial intelligence methods also in the medical field for the automation of tasks (e.g. automatic segmentation) and classification (e.g. tumor aggressiveness). The extraction of quantitative data, and more generally the study of tumor lesions, requires manual segmentation by one or more doctors. This process requires very long times as each image must be processed individually; furthermore, the result also depends on the level of experience of the doctor carrying out the segmentation and this could create a source of heterogeneity, affecting the reproducibility of the segmentation. AI-based automatic segmentation methods can be applied to medical images for the localization of tumor lesions, thus exceeding the limits of manual segmentation.
Among all the patients who underwent PET/CT with choline at our Institute between 2004 and 2007 to restage the prostatic disease following biochemical recovery of the disease, in this retrospective study the patients previously treated with radical prostatectomy, who present a progressive increase in the PSA value in the absence of hormone therapy and of which there is knowledge of the main clinical and follow-up data, with particular attention to survival data. The PET/CT study with Choline, being part of the normal standard diagnostic work-up of patients, was performed following the normal clinical protocol. With the retrospective analysis of the data, the time elapsed following the prostatectomy operation and the follow-up time after the Choline PET/CT study will be evaluated and patients who died due to prostate cancer will therefore be considered. Prostate cancer-specific survival, calculated as the interval between radical prostatectomy and death due to prostate cancer, will be used as the end point. The differences between prostate cancer-specific survival of patients with a positive choline PET/CT study and patients with a negative choline PET/CT study will be evaluated (log-rank test). Choline PET/CT will be considered positive if pathological findings with significant tracer uptake are identified. In the subpopulation of patients with a positive Choline PET/CT study, survival data will also be evaluated in relation to the site of positivity of the PET/CT study, in particular at the level of local recurrence, in the lymph node or skeletal site.
Assess whether introducing an educational video regarding the benefits, risks, and limitations of genetic testing affects prostate cancer patients' decisional conflict regarding receiving germline genetic testing.
the increase in the serum PSA (prostate specific antigen) value following radical treatment commonly involves subsequent treatment which, in the absence of morphological evidence of disease recovery, is conducted empirically through local radiotherapy or systemic hormonal therapy. The use of PET with choline is therefore of extreme clinical interest as it allows to identify the site of disease recurrence, thus being able to direct towards a specific therapeutic treatment. The diagnostic accuracy of choline PET in identifying the location of the disease has been widely demonstrated in the literature and is comparable to those of conventional diagnostic methods previously described for the restaging of patients with prostatic disease. The real advantage of this method is the possibility of obtaining the same information as conventional methods by carrying out a single exam.
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.
The purpose of this study is to learn about how long novel hormonal therapies are taken by men to treat mCSPC. Novel hormonal therapies in this study include study medicines abiraterone, apalutamide, and enzalutamide. Prostate cancer is one of the most common cancers in men. The prostate is a gland in the male body that helps make semen. Metastatic cancer is a cancer that has spread to other parts of the body. Castration-sensitive prostate cancer means the cancer is being controlled by keeping the testosterone levels as low as would be expected if the testicles were removed by surgery. This is a real-world study, not a clinical trial. This means that researchers will look at what happens when men receive the treatments prescribed by their own doctor as part of their usual healthcare treatment. In this study, researchers will use insurance claim information from Medicare claims data. The study will include patients' information from the database for men who: - Were identified to have mCSPC. - Started treatment with novel hormonal therapy (index date) for mCSPC. - Were 65 years of age or older one year before index date. Men in this study will be taking novel hormonal therapy for treatment of their mCSPC. We will describe how long men take novel hormonal therapy. This study will use patient information from insurance claims. It will take information one year before start of novel hormonal treatment until the end of insurance period or until information is available.
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.