View clinical trials related to Ovarian Neoplasms.
Filter by:The goal of this type of clinical trial study is to evaluate the safety and efficacy of metabolic remodeling nature killer cells as neoadjuvant therapy in newly diagnosed patients with advanced ovarian cancer
This is a two-part, open-label, multicenter, dose escalation and dose expansion study designed to evaluate the safety, tolerability, pharmacokinetics (PK), pharmacodynamics (PDx), and anti- tumor activity of ETX-19477, a novel reversible small molecule inhibitor of PARG.
The researchers are doing this study to find out whether the combination of avutometinib, defactinib, and letrozole is an effective treatment for people with low-grade serous ovarian cancer (LGSOC). The researchers will also look at the safety of this combination.
The purpose of this study is to determine if the experimental treatment with poly-ADP ribose polymerase (PARP) inhibitor, ACE-86225106 is safe, tolerable and has anti-cancer activity in adult patients with advanced solid tumors.
This study evaluates how lifestyle modifications that may be made to manage chemotherapy side effects in patients with gynecologic malignancies.
The goal of this clinical trial is as follows:(1) Establish a clinical technical system for ctDNA dynamic monitoring of MRD in postoperative EOC patients, providing a new technical means for postoperative recurrence prevention and monitoring of EOC patients.(2) Establish a clinical technical system for adjuvant treatment of postoperative recurrence prevention for EOC patients with conventional protocols combined with personalized vaccines, so as to provide a new treatment method for postoperative recurrence prevention for EOC patients, with a view to obtaining a better survival prognosis.(3) To establish and improve the prediction process of Neoantigen for ovarian cancer and the in vitro evaluation system of the effectiveness of neoantigen vaccine, achieve independent innovation of tumor neoantigen vaccine treatment technology, and cultivate a group of technical forces to master the development of modern tumor vaccine drugs.(4) The new technology system has been promoted and applied in 5 hospitals in the province.
To evaluate if CT features at diagnosis in patients with HGSOC can be used to build an Artificial Intelligence model capable of discerning the pathological involvement of the mesentery, assessing the potential impediments for an optimal debulking surgery and predicting the development of resistance to platinum based chemotherapeutic agents.
The biological spatial and temporal heterogeneity of High Grade Serous Ovarian Carcinoma (HGSOC) severely impacts the effectiveness of therapies and is a determinant of poor outcomes. Current histological evaluation is made on a single tumour sample from a single disease site per patient thus ignoring molecular heterogeneity at the whole-tumour level, key for understanding and overcoming chemotherapy resistance. Imaging can play a crucial role in the development of personalised treatments by fully capturing the disease's heterogeneity. Radiomics quantify the image information by capturing complex patterns related to the tissue microstructure. This information can be complemented with clinical data, liquid biopsies, histological markers and genomics ("radiogenomics") potentially leading to a better prediction of treatment response and outcome. However, the extracted quantitative features usually represent the entire tumour, ignoring the spatial context. On the other hand, radiomics-derived imaging habitats characterize morphologically distinct tumour areas and are more appropriate for monitoring the changes in the tumour microenvironment over the course of therapy. In order to successfully incorporate the habitat-imaging approach to the clinic, histological and biological validation are crucial. However, histological validation of imaging is not a trivial task, due to issues such as unmatched spatial resolution, tissue deformations, lack of landmarks and imprecise cutting. Patient-specific three-dimensional (3D) moulds are an innovative tool for accurate co-registration between imaging and histology. The aim of this study is to optimize and integrate such an automated computational 3D-mould co-registration approach in the clinical work-flow in patients with HGSOC. The validated radiomics-based tumour habitats will also be used to guide tissue sampling to decipher their underlying biology using genomics analysis and explore novel prediction markers.
The present study aims to collect early bright field image of patient-derived organoids with ovarian cancer. By leveraging artificial intelligence, this study will seek to construct and refine algorithms that able to predict growth of ovarian cancer organoids.
The purpose of this study is to find out how many participants are interested in a surgical preventive procedure after watching an educational video. Before and after watching the video, participants will complete questionnaires in the clinic.