PET imaging biomarkers predict survival in peripheral T-cell lymphoma: A systematic review and meta-analysis.
Bibliographic record
Abstract
Abstract Background Peripheral T-cell lymphomas (PTCLs) are aggressive and heterogeneous lymphoid malignancies with poor prognoses and limited response durability using CHOP-based regimens. Recent studies suggest that PET/CT-derived biomarkers—including total metabolic tumor volume (TMTV), total lesion glycolysis (TLG), and Deauville scores (DS) at interim and end-of-treatment (EOT)—may hold prognostic value, yet they are not uniformly incorporated into clinical decision-making. As frontline regimens like BV-CHP become more widely adopted, identifying reliable predictors of outcomes is critical. We hypothesized that PET-based biomarkers—TMTV, TLG, and DS—are significantly associated with overall survival (OS) and progression-free survival (PFS) in patients with nodal PTCL treated with CHOP-like or BV-CHP regimens. Methods This systematic meta-analysis (PROSPERO CRD420251041744) identified 196 articles via systematic search of PubMed and Cochrane searches. Inclusion criteria were treatment-naive adults with nodal PTCL treated with CHOP-like BV-CHP regimens studies reporting hazard ratios (HRs) for OS or PFS associated with baseline TMTV, TLG, or DS. Exclusion criteria included case reports, pediatric studies, non-English publications, and studies involving other lymphoma subtypes, ENKTL, cutaneous T-cell lymphomas, and other mature lymphoid neoplasms. Ten studies met the criteria. Five reviewers screened and extracted data. PET variables were dichotomized as study specific threshold of high vs. low (TMTV,) and DS 4–5 vs. 1–3 for interim and EOT comparisons. We conducted Meta-analyses using RevMan 7.2.0 software, inverse-variance methods and random-effects models. Heterogeneity was assessed via I² and τ² (REML). Results Out of 10 eligible studies 9 were retrospective, and ECHELON-2 (E-2) study was prospective with prespecified assessment of PET response using DS. CHOP-like regimens were used in 1075 patients (including 226 from the matched E-2 cohort), while 226 were treated with BV-CHP in E2. The pooled median age was 58.5 years, predominantly male (61.8%) with advanced-stage disease (88.6% Stage III/IV). Patient distribution between low-risk (IPI 0-2) and high-risk (IPI 3-5) groups was balanced. Follow-up duration ranged from 17 to 65.8 months; the estimated pooled median follow-up was 38 months. Baseline TMTV was evaluated in seven studies (n = 680) and was significantly associated with inferior PFS (pooled HR 2.78 [95% CI: 1.24–6.24]; p = 0.01; I² = 88%). Baseline TMTV also correlated with OS across five studies (HR 2.43 [95% CI: 1.49–3.97]; p = 0.0004; I²=83 %). TLG, reported in three studies (n = 254), was associated with worse OS (HR 3.07 [95% CI: 1.29–7.34]; p = 0.01; I² = 73%). Higher baseline SUV was variably associated with OS and PFS across studies, but without consistent statistical significance. DS high at interim PET (6 studies; n = 698) was strongly associated with PFS (HR 3.40 [95% CI: 2.54–4.54]; p < 0.00001; I² = 13%) and OS (HR 3.62 [95% CI: [2.05- 6.38]; p < 0.00001; I²= 65%). DS high at end-of-treatment (EOT) was even more predictive of OS (pooled HR 5.89 [95% CI: 2.15-16.14]; p < 0.0006; I² = 66%) and PFS (HR 4.29 [95% CI: 1.96–9.39]; p < 0.0003; I²=61 %). DS 1–3 was consistently associated with durable remissions, while DS 4–5 often identified primary refractory . Conclusions This meta-analysis confirms that PET-based imaging biomarkers—particularly TMTV, TLG, and DS are significantly associated with OS and PFS in patients with nodal PTCL treated with CHOP or BV-CHP. DS 4–5 at EOT showed the strongest predictive value across cohorts. These findings support incorporating PET biomarkers into clinical risk models and future trials. Limitations include inter-study heterogeneity, mostly retrospective designs, variability in PET methods and cutoff definitions. Prospective validation of optimal cutoffs, and integration with molecular classifiers are needed to refine risk-adapted therapy in PTCL.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".