A deep learning PET/CT biomarker for early progression (POD24) and survival stratification in follicular lymphoma: a multicenter study.

Abstract

OBJECTIVE: To develop and validate a prognostic imaging biomarker derived from baseline [¹⁸F]FDG PET/CT using tabular deep learning for prediction of progression of disease within 24 months (POD24) and survival risk stratification in patients with follicular lymphoma (FL). METHODS: This retrospective multicenter study included 309 patients with newly diagnosed FL (grades 1-3a) from five independent medical centers. Tumor volumes segmented from baseline [¹⁸F]FDG PET and CT images were used to extract high-throughput radiomic features. Five conventional machine learning algorithms and four advanced tabular deep learning models were developed and compared. The predictive output of the GAMformer model was defined as the deep learning score (DLS). The DLS was integrated with clinical variables and PET metabolic parameters to construct a multiparametric model in the training cohort. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis, and further validated in validation cohort. RESULTS: During a median follow-up of 44 months, POD24 occurred in 55 patients. The DLS demonstrated strong predictive performance for POD24 (training AUC = 0.857; validation AUC = 0.753). The multiparametric model further improved discrimination, achieving AUCs of 0.882 in the training cohort and 0.797 in the validation cohort, outperforming FLIPI, FLIPI-2, and PRIMA-PI. Calibration showed good agreement, and decision curve analysis indicated higher net clinical benefit. The DLS stratified survival risk (P < 0.05) and remained predictive of survival and POD24 across histologic grades. CONCLUSIONS: The DLS derived from baseline [¹⁸F]FDG PET/CT enables POD24 prediction and accurate survival risk stratification in FL, supporting its potential role in precision management.