This invention discloses a non-invasive systemic assessment method and
system for predicting the transformation of
follicular lymphoma. The method includes the following steps: S1, obtaining four characteristics of the subject:
peripheral blood
hepatitis B surface
antigen (
HBsAg), neutrophil-to-
lymphocyte ratio (NLR), serum
lactate dehydrogenase (LDH), and PET-CT reporting maximum standardized uptake (SUVmax); S2, determining the weight of each characteristic based on its importance, with the weights from high to low being
HBsAg, LDH, NLR, and SUVmax, and converting each characteristic into a risk
score of 0-3 points using a unique fixed node; S3, weighted summing of the scores of the four characteristics to obtain a total
score S, establishing a correspondence between the total
score and transformation, constructing a web-based
calculator and transformation
scoring system, and outputting the individual transformation probability prediction result. By embedding the four conventional indicators (
HBsAg, NLR, LDH, and SUVmax) selected by LASSO and Boruta into a lightweight CatBoost model, the transformation probability can be output, realizing a non-invasive assessment of the histological transformation risk of
follicular lymphoma at initial diagnosis.