Clinical Research Directory

Study of Olaparib Maintenance Following Cabazitaxel-Carbo in Men With AVPC

The goal of this clinical research study is to learn if olaparib, when given after treatment with cabazitaxel, carboplatin, and prednisone, can help to control aggressive variant prostate cancer (AVPC). The safety of these drugs will also be studied. This is an investigational study. Cabazitaxel and carboplatin are FDA approved and commercially available for the treatment of certain types of prostate cancer. Prednisone is FDA-approved and commercially available as a corticosteroid. Olaparib is FDA approved and commercially available for the treatment of certain types of ovarian cancer. The combination of cabazitaxel and carboplatin followed by olaparib in this study is investigational. The study doctor can describe how the study drugs are designed to work. Up to 96 participants will be enrolled on this study. All will take part at MD Anderson.

An AI Platform Integrating Imaging Data and Models, Supporting Precision Care Through Prostate Cancer's Continuum

In Europe, prostate cancer (PCa) is the second most frequent type of cancer in men and the fifth most lethal. Current clinical practices, often leading to overdiagnosis and overtreatment of indolent tumors, suffer from lack of precision calling for advanced AI models to go beyond SoA by deciphering non-intuitive, high-level medical image patterns and increase performance in discriminating indolent from aggressive disease, early predicting recurrence and detecting metastases or predicting effectiveness of therapies. To date efforts are fragmented, based on single-institution, size-limited and vendorspecific datasets while available PCa public datasets (e.g. US TCIA) are only few hundred cases making model generalizability impossible. The ProCAncer-I project brings together 20 partners, including PCa centers of reference, world leaders in AI and innovative SMEs, with recognized expertise in their respective domains, with the objective to design, develop and sustain a cloud based, secure European Image Infrastructure with tools and services for data handling. The platform hosts the largest collection of PCa multi-parametric (mp)MRI, anonymized image data worldwide (\>14,000 cases), based on data donorship, in line with EU legislation (GDPR). Robust AI models are developed, based on novel ensemble learning methodologies, leading to vendor-specific and -neutral AI models for addressing 8 PCa clinical scenarios. To accelerate clinical translation of PCa AI models, we focus on improving the trust of the solutions with respect to fairness, safety, explainability and reproducibility. Metrics to monitor model performance and a causal explainability functionality are developed to further increase clinical trust and inform on possible failures and errors. A roadmap for AI models certification is defined, interacting with regulatory authorities, thus contributing to a European regulatory roadmap for validating the effectiveness of AI-based models for clinical decision making.

Active Surveillance SNEP Assay Registry Trial for Prostate Cancer

A multi-center, prospective active surveillance registry trial assessing the performance of a non-invasive blood test for indolent prostate cancer disease management.

Artificial Intelligence Models for Precision Prediction and Treatment of Prostate Cancer

The aim of this clinical trial is whether artificial intelligence models can be used for accurate clinical preoperative diagnosis and postoperative diagnosis of pathological findings, and will also measure the accuracy of the predictions made by the artificial intelligence models.The main target questions addressed by the model building are: 1. whether the AI model can learn from preoperative MRI and postoperative Whole Slide Images so as to accurately predict information such as benignness or malignancy, aggressiveness, grading, subtypes, genes, etc. for participants suspected of having prostate cancer preoperatively/puncturally. 2. whether the AI model is capable of learning postoperative macropathology slides to enable outcome diagnosis of surgical pathology slides in new participants. Participants will: 1. complete an MRI examination and have their MRI images analysed by the established AI model to make an accurate diagnosis of them. 2. Based on the diagnosis, if prostate cancer is predicted, they will undergo radical prostate cancer surgery and refine their surgical pathology.