Your browser doesn't support javascript.
loading
Investigating and modeling positron emission tomography factors associated with large cell transformation from low-grade lymphomas.
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.
Afiliación
  • Obeid JP; Department of Radiation Oncology Stanford University School of Medicine Stanford California USA.
  • Hiniker SM; Department of Radiation Oncology Stanford University School of Medicine Stanford California USA.
  • Schroers-Martin J; Department of Medicine Division of Oncology, Stanford University School of Medicine Stanford California USA.
  • Guo HH; Department of Radiology Stanford University School of Medicine Stanford California USA.
  • No HJ; Department of Radiation Oncology Stanford University School of Medicine Stanford California USA.
  • Moding EJ; Department of Radiation Oncology Stanford University School of Medicine Stanford California USA.
  • Advani RH; Department of Medicine Division of Oncology, Stanford University School of Medicine Stanford California USA.
  • Alizadeh AA; Department of Medicine Division of Oncology, Stanford University School of Medicine Stanford California USA.
  • Hoppe RT; Department of Radiation Oncology Stanford University School of Medicine Stanford California USA.
  • Binkley MS; Department of Radiation Oncology Stanford University School of Medicine Stanford California USA.
EJHaem ; 4(1): 90-99, 2023 Feb.
Article en En | MEDLINE | ID: mdl-36819184
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.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: EJHaem Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: EJHaem Año: 2023 Tipo del documento: Article
...