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

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 match...

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Detalles Bibliográficos
Autores principales: Obeid, Jean‐Pierre, Hiniker, Susan M., Schroers‐Martin, Joseph, Guo, H. Henry, No, Hyunsoo Joshua, Moding, Everett J., Advani, Ranjana H., Alizadeh, Ash A., Hoppe, Richard T., Binkley, Michael S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928791/
https://www.ncbi.nlm.nih.gov/pubmed/36819184
http://dx.doi.org/10.1002/jha2.615
Descripción
Sumario: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.