Investigating and modeling positron emission tomography factors associated with large cell transformation from low‐grade lymphomas

Abstract

Abstract Low‐grade lymphomas have a 1%–3% annual risk of transformation to a high‐grade histology, and prognostic factors remain undefined. We set to investigate the role of positron emission tomography (PET) metrics in identification of transformation in a retrospective case‐control series of patients matched by histology and follow‐up time. We measured PET parameters including maximum standard uptake value (SUV‐max) and total lesion glycolysis (TLG), and developed a PET feature and lactate dehydrogenase (LDH)‐based model to identify transformation status within discovery and validation cohorts. For our discovery cohort, we identified 53 patients with transformation and 53 controls with a similar distribution of follicular lymphoma (FL). Time to transformation and control follow‐up time was similar. We observed a significant incremental increase in SUV‐max and TLG between control, pretransformation and post‐transformation groups (P < 0.05). By multivariable analysis, we identified a significant interaction between SUV‐max and TLG such that SUV‐max had highest significance for low volume cases (P = 0.04). We developed a scoring model incorporating SUV‐max, TLG, and serum LDH with improved identification of transformation (area under the curve [AUC] = 0.91). Our model performed similarly for our validation cohort of 23 patients (AUC = 0.90). With external and prospective validation, our scoring model may provide a specific and noninvasive tool for risk stratification for patients with low‐grade lymphoma.

Keywords

• lymphomas
• mathematical modeling
• pet