Retrospective cohort study with internal and external validation of a predictive tool for POD24 risk assessment in follicular lymphoma patients

Follicular lymphoma (FL) is a slow-growing cancer of the immune system that affects white blood cells called lymphocytes.. Based on histology FL is graded as 1 - 3 (low grade to high grade). Treatment patterns for patients with follicular lymphoma grade 3a (FL3a) remain controversial now. Currently, National Comprehensive Cancer Network (NCCN) guidelines recommended some patients are treated as FL and others are treated as Diffuse large B cell lymphoma (DLBCL), which is an aggressive, fast-growing lymphoma and is the most common type of blood cancer. However, which patients should be treated as FL or DLBCL remains poorly defined. Therefore, it is important to perform risk stratification for those patients to guide therapy decisions for FL3a. Using a novel machine learning algorithm, we constructed a risk stratification tool mainly based on the clinical features in the derivation cohort of 543 FL3a patients and compared with conventional risk models. This machine learning model was developed to discriminate FL3a patients into low- and high-risk groups, which will help clinicians select individual treatment strategies.