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

What this Trial Is About

Researchers are conducting a global observational study to understand mature T-cell and NK-cell neoplasms (TNKL), which are types of lymphomas. The study aims to link molecular changes in the tumors to patient outcomes like overall survival and treatment response. It also explores how machine learning can help identify genetic factors that influence how patients respond or resist treatments, moving toward more personalized care. This study enrolls patients who are newly diagnosed or have relapsed/refractory TNKL from multiple institutions worldwide. Participants will be followed for up to four years during their clinical management. Researchers will collect data on demographics, clinical features, pathology, molecular tumor details, imaging, treatments, and quality of life. Genetic testing including whole exome and RNA sequencing will be done on tumor samples and other biological materials to provide a comprehensive molecular profile. Participants will have routine clinical visits where data and samples will be collected by research teams. The study will monitor key outcomes like overall survival, progression-free survival, response duration, and adverse events over four years. Data will be securely shared among sites, and advanced deep learning models will analyze the molecular information alongside clinical outcomes to predict patient responses and survival across different lymphoma subtypes and treatments.

A Global Study of the PETAL Consortium