This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.
The goal of this observational study is to correlate molecular alterations with outcomes including overall survival (OS), progression-free survival (PFS), response rates for patients with a new diagnosis, primary refractory or relapse, of mature T-cell and NK-cell neoplasms (TNKL). We hypothesize that machine learning can be leveraged to uncover distinct genetic vulnerabilities that underlie treatment response and resistance for patients with TNKL, thus moving towards personalized treatment solutions.
Integration of Machine Learning and Genomics to Predict Outcomes for Newly Diagnosed, Relapsed and Refractory Mature T-cell and NK/T-cell Lymphomas: a Global Study of the PETAL Consortium
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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Sponsor: Massachusetts General Hospital
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.