View clinical trials related to Blood Cancer.
Filter by:The Leukemia and Lymphoma Society (LLS) has built a National Research Registry to evaluate real world experiences and medical outcomes for people with blood cancer, before, during, and after blood cancer treatments.
To the best of our knowledge, BELUGA will be the first prospective trial investigating the usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and digitalized blood-smears, categorization of yet undiagnosed patient samples will prospectively be compared to current state-of-the-art diagnosis at the Munich Leukemia Laboratory (hereafter MLL). In total, a collection of 25,000 digitalized blood smears and 25,000 flow cytometry datapoints will be prospectively used to train an AI-based deep neuronal network for correct categorization. Subsequently, the superiority will be challenged for the primary endpoints: sensitivity and specificity of diagnosis, most probable diagnosis, and time to diagnose. The secondary endpoints will compare the consequences regarding further diagnostic work-up and, thus, clinical decision making between routine diagnosis and AI guided diagnostics. BELUGA will set the stage for the introduction of AI-based hematologic diagnostics in a real-world setting.
This study will validate a previously developed pediatric prognostic biomarker algorithm aimed at improving prediction of risk for the later development of chronic graft-versus-host disease (cGvHD) in children and young adults undergoing allogeneic hematopoietic stem cell transplant. By developing an early risk stratification of patients into low-, intermediate-, and high-risk for future cGvHD development (based upon their biomarker profile, before the onset of cGvHD), pre-emptive therapies aimed at preventing the onset of cGvHD can be developed based upon an individual's biological risk profile. This study will also continue research into diagnostic biomarkers of cGvHD, and begin work into biomarker models that predict clinical response to cGvHD therapies.