This multicenter study aims to evaluate the predictive value of tumor microenvironment PET/CT imaging for treatment response and prognosis in patients with aggressive lymphoma. Eligible patients with histologically confirmed aggressive lymphoma will undergo FAPI PET/CT imaging, and selected patients who are candidates for immunotherapy will undergo Grazytracer PET/CT imaging. These imaging methods are intended to assess fibroblast activation protein expression and granzyme B activity in lymphoma lesions, reflecting cancer-associated fibroblasts and cytotoxic T-cell activity in the tumor microenvironment. The study will explore optimal imaging assessment criteria and develop clinical-pathological-imaging models to predict treatment response, prognosis, and the relationship between tumor heterogeneity and refractory or relapsed disease.
Histologically confirmed aggressive lymphoma.
Baseline PET/CT showing moderate to high FDG uptake in lymphoma lesions.
No previous history of malignant tumors.
No significant cardiac, hepatic, or renal dysfunction.
Low FDG uptake on baseline PET/CT.
Lymphoma lesions have been surgically removed, with no positive lesions available for evaluation.
Active or uncontrolled chronic inflammation or infection.
Uncontrolled diabetes mellitus.
Single group
Al18F-NOTA-FAPI
DrugAl18F-NOTA-FAPI was injected into the patients before the PET/CT scans
Al18F-NOTA-grazytracer/68Ga-grazytracer
DrugAl18F-NOTA-grazytracer/68Ga-grazytracer was injected into the patients before the PET/CT scans
Predictive Performance of FAPI PET/CT for Treatment Response or Disease Progression
The predictive performance of FAPI PET/CT imaging parameters for treatment response or disease progression in patients with aggressive lymphoma will be evaluated. Imaging parameters, including visual assessment and semi-quantitative parameters such as SUVmax, tumor-to-background ratios, metabolic tumor volume, total lesion glycolysis, and SUV heterogeneity, will be analyzed using receiver operating characteristic curve analysis. The area under the curve, sensitivity, specificity, positive predictive value, and negative predictive value will be calculated.
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