Purpose <p>Large B-cell lymphomas (LBCL) include diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS), and subtypes such as transformed non-Hodgkin’s lymphoma (tNHL), primary mediastinal B-cell lymphoma (PMBL), and double/triple-hit lymphomas (DHL/THL). While metabolic risk scores based on metabolic tumor volume (MTV) have demonstrated prognostic value in DLBCL, NOS, their applicability to other LBCL subtypes remains unclear.</p> Methods <p>Baseline [<sup>18</sup>F]FDG-PET/CT scans of LBCL patients treated with R-CHOP regimens at our institution were retrospectively analyzed. Metabolic parameters, including MTV, lesion dissemination (SDmax) and tumor surface volume ratio were calculated for each histological subgroup. Four metabolic risk scores–the international metabolic prognostic index (IMPI), MTV/WHO PS, MTV/SDmax and Clinical PET model- were applied to calculate progression risk in LBCL subtypes. Harrell’s C-index evaluated the prognostic performance. A multivariable model was developed for tNHL.</p> Results <p>We included tNHL (<i>n</i> = 88), DHL/THL (<i>n</i> = 32), PMBL (<i>n</i> = 26) and others (<i>n</i> = 49), and compared them to a cohort of previously published DLBCL, NOS (<i>n</i> = 355). IMPI demonstrated the highest C-index amongst the metabolic risk scores in tNHL for progression-free survival (PFS), overall survival, time to progression and progression of disease within 12 months, but was outperformed by the IPI. For DHL/THL the highest C-indices were observed for MTV/WHO PS. For PMBL the clinical PET score showed the highest C-indices. SDmax improved prognostic predictions in PMBL and tNHL, but not in DHL/THL. Multivariate analysis identified independent predictors of PFS in tNHL, including IPI and SUV<sub>mean</sub>.</p> Conclusion <p>Metabolic risk scores show variable prognostic value across LBCL subtypes. Subtype-specific metabolic models may enhance personalized risk stratification and guide treatment approaches.</p>

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Divergent prognostic utility of metabolic risk scores in large B-cell lymphoma subtypes: a real-world analysis

  • Vibeke K. J. Vergote,
  • Gregor Verhoef,
  • Ann Janssens,
  • F. J. S. Woei-A-Jin,
  • Wies Deckers,
  • Annouschka Laenen,
  • Thomas Tousseyn,
  • Daan Dierickx,
  • Christophe M. Deroose

摘要

Purpose

Large B-cell lymphomas (LBCL) include diffuse large B-cell lymphoma, not otherwise specified (DLBCL, NOS), and subtypes such as transformed non-Hodgkin’s lymphoma (tNHL), primary mediastinal B-cell lymphoma (PMBL), and double/triple-hit lymphomas (DHL/THL). While metabolic risk scores based on metabolic tumor volume (MTV) have demonstrated prognostic value in DLBCL, NOS, their applicability to other LBCL subtypes remains unclear.

Methods

Baseline [18F]FDG-PET/CT scans of LBCL patients treated with R-CHOP regimens at our institution were retrospectively analyzed. Metabolic parameters, including MTV, lesion dissemination (SDmax) and tumor surface volume ratio were calculated for each histological subgroup. Four metabolic risk scores–the international metabolic prognostic index (IMPI), MTV/WHO PS, MTV/SDmax and Clinical PET model- were applied to calculate progression risk in LBCL subtypes. Harrell’s C-index evaluated the prognostic performance. A multivariable model was developed for tNHL.

Results

We included tNHL (n = 88), DHL/THL (n = 32), PMBL (n = 26) and others (n = 49), and compared them to a cohort of previously published DLBCL, NOS (n = 355). IMPI demonstrated the highest C-index amongst the metabolic risk scores in tNHL for progression-free survival (PFS), overall survival, time to progression and progression of disease within 12 months, but was outperformed by the IPI. For DHL/THL the highest C-indices were observed for MTV/WHO PS. For PMBL the clinical PET score showed the highest C-indices. SDmax improved prognostic predictions in PMBL and tNHL, but not in DHL/THL. Multivariate analysis identified independent predictors of PFS in tNHL, including IPI and SUVmean.

Conclusion

Metabolic risk scores show variable prognostic value across LBCL subtypes. Subtype-specific metabolic models may enhance personalized risk stratification and guide treatment approaches.