Considering a clinical trial is an important option when deciding on your treatment course of action. If interested in the study detailed below, please follow up with your care team to learn if it may be right for you. Note that the enrollment status of the study may change.
Primary Objective: -To compare the PFS of a standard chemotherapy approach versus an IO therapy approach (brentuximab vedotin and nivolumab) in patients with newly diagnosed early stage cHL who have a rapid early response (RER) as determined by PET2 after 2 cycles of doxorubicin, bleomycin, vinblastine, dacarbazine (ABVD) chemotherapy -To compare the PFS of a standard chemotherapy approach versus an IO therapy approach (brentuximab vedotin and nivolumab) plus involved site radiation therapy (ISRT) in patients with newly diagnosed early stage cHL who have a slow early response (SER) as determined by PET2 after 2 cycles of ABVD chemotherapy. Secondary Objectives: -To demonstrate non-inferiority of overall survival (OS) at 12 years of IO therapy versus standard therapy in early stage cHL patients who have a RER as determined by PET2 after 2 cycles of doxorubicin, bleomycin, vinblastine, dacarbazine (ABVD) chemotherapy. -To evaluate the overall survival (OS) at 12 years of IO therapy versus standard therapy in early stage cHL patients who have a SER as determined by PET2 after 2 cycles of doxorubicin, bleomycin, vinblastine, dacarbazine (ABVD) chemotherapy. -To demonstrate non-inferiority of overall survival (OS) at 12 years of IO therapy versus standard therapy in early stage cHL patients. -To evaluate in patients with newly diagnosed early stage cHL the PFS of a standard chemotherapy approach versus an IO therapy approach (brentuximab vedotin and nivolumab) in the overall cohort, in the favorable risk cohort, and in the unfavorable risk cohort. -To evaluate the EFS at 12 years of patients undergoing standard chemotherapy versus an IO therapy approach (brentuximab vedotin and nivolumab). -To compare the physician-reported treatment-related adverse event (AE) rates between a standard chemotherapy approach and an IO therapy approach (brentuximab vedotin and nivolumab) in patients with newly diagnosed early stage cHL. -To compare patient-reported adverse events using pediatric and adult versions of Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE), stratified by age groups, therapeutic arms, and receipt of RT over time. -To evaluate changes in patient-reported fatigue, cognitive functioning, and health-related quality of life (HRQoL), e.g., emotional, physical, and role functioning, by treatment arm, using validated adult and pediatric measurement systems. -To evaluate self-reported late morbidities (e.g., cardiovascular, pulmonary and endocrine) over time for children, adolescents and adults undergoing standard chemotherapy versus an IO therapy approach (brentuximab vedotin and nivolumab) with and without RT using measures from the St. Jude Lifetime Cohort Study (SJLIFE). -To evaluate FDG-PET measurements of metabolic tumor burden (MTV and TLG) at PET1 as a predictive marker of PFS. -To evaluate the associations between race/ethnicity and key outcomes including early response to therapy, PFS and OS Exploratory Objectives: -To evaluate the PFS of a standard chemotherapy approach versus an IO therapy approach (brentuximab vedotin and nivolumab) in patients with newly diagnosed early stage cHL across different age groups (ages 5-11 years, 12-21 years, 22-39 years, 40-60 years). To bank specimens for future correlative studies. -To assess concordance and discordance of rapid central review and local institutional review of FDG PET 5-point score (5-PS; previously referred to as Deauville score) at baseline PET1, interim PET2 and end of systemic therapy PET-EST SER. -To assess the association between PFS and the quantitative FDG-PET/CTnparameters (PET MTV, TLG, delta-SUV and PET SUV-based quantitative surrogates (qPET) of visual qualitative 5-PS) on measurements by automated measurements using convolutional neural networks (CNNs) through artificial intelligence (AI) machine learning in the entire population.