Prognostic value of baseline metabolic tumor volume from lysa ro-CHOP trial inpatients with previously untreated peripheral T-cell lymphoma

Abstract

Abstract Background: Peripheral T-cell lymphomas (PTCLs) are aggressive lymphoid malignancies with poor outcomes and limited prognostic tools beyond the International Prognostic Index (IPI). While total metabolic tumor volume (TMTV) has been well established as a major prognostic factor in diffuse large B-cell lymphoma, evidence remains limited in peripheral T-cell lymphoma (PTCL). We performed an ancillary analysis of the RO-CHOP trial to investigate the prognostic impact of TMTV and its added value over IPI. Methods: The Ro-CHOP trial was a multicenter randomized phase III study showing no increased efficacy of Ro-CHOP versus CHOP as frontline treatment of PTCL patients. A post-hoc analysis was conducted on patients from France and Belgium centers with PET/CT available for review. TMTV was computed using the SUV4.0 method on LIFEx software. Maxstat package of R software was used to find the best TMTV cutoff based progression-free survival (PFS) and overall survival (OS) data.The thresholds were identified by splitting the data into 80% for training and 20% for validation. The prognostic value of the TMTV was assessed using Cox proportional hazards models, using both categorical and continuous forms (using restricted cubic splines) of the TMTV. We evaluated the improvement in discriminatory performance of TMTV (cutoff and splines) added to the IPI model by calculating time-dependent AUCs based on PFS and OS data. Leave-one-out cross-validation method (LOOCV) was used to prevent overfitting and reduce bias for AUC's calculation. Results: From 286 patients, 43 had no baseline PET/CT available, resulting in a final study population of 243. The median age was 65 years (interquartile range 57 to 70), most patients had Ann Arbor stage III–IV (83.5%) and 47% had a follicular helper T-cell (TFH) phenotype, with no significant difference between the 43 patients (with no PET available) and the PET cohort. The median pre-therapy TMTV was 171 cm³ (IQR: 29–574). Patients with high TMTV (>197 mL) had inferior PFS compared to those with lower TMTV (HR = 1.67, 95% CI: 1.25–2.23; P = 0.0005), with a 2-year PFS of 25% vs 41%, respectively. Similarly, TMTV >310 mL was associated with poorer overall survival (OS) (HR = 1.86, 95% CI: 1.35–2.58; P = 0.0002), with 2-year OS rates of 44% vs 68%. Adding TMTV (in class) to the IPI model improved prognostic discrimination as reflected by consistently higher time-dependent AUCs for overall survival. At 20 months, the AUC increased from 67.7 (IPI alone) to 69.8 (IPI+TMTV) , and at 80 months, from 64.5 to 68.6. Continuous TMTV modeling (splines) further enhanced prognostic accuracy with AUC at 20 months of 70.4 and at 80 months of 67.8.The AUC's improvement for PFS analysis was weaker in the global population. In OS multivariable analysis, high TMTV remained an adverse factor (HR = 1.54 [1.09;2.19] p=0.015), with age > 60 y (HR = 1.79 [1.18;2.71] P=0.006), and the presence of > 2 extranodal sites (HR = 1.74 [1.21;2.49] P=0.002). While median TMTV values did not differ significantly between TFH and non-TFH lymphomas (p=0.32), the prognostic impact of TMTV (class) on PFS differed significantly by TFH status (interaction test: p = 0.0267). TMTV was a strong prognostic factor in non-TFH patients in univariate (HR = 2.22; 95% CI: 1.49–3.32; p <0.0001) and in multivariate analysis (HR= 1.91; 95% CI: 1.18-3.11; p=0.009). In this specific subgroup adding TMTV to IPI model led to an improved prognostic discrimination. At 20 months, the AUC increased from 66.6 (IPI alone) to 70.16 (IPI+TMTV) , and at 70 months, from 67.6 to 73. No statistically significant association was observed in TFH subgroup. Conclusions: Baseline TMTV measured by PET is an independent and robust prognosticator of survival in PTCL patients, offering incremental prognostic value when integrated with established indices such as IPI. These findings advocate for the integration of metabolic imaging biomarkers into routine risk stratification and treatment planning in PTCL.