Prognostic Value of Multiple Manual Segmentation Methods for Diffuse Large B-Cell Lymphoma with 18F-FDG PET/CT
Bibliographic record
Abstract
Quantitative 18F-FDG PET/CT-derived metabolic metrics are strongly associated with patient outcomes in diffuse large B-cell lymphoma (DLBCL), but the lack of consensus on optimal segmentation thresholds limits standardization. This study evaluated the prognostic value of various metabolic tumor volume (MTV) segmentation approaches in 140 stage II–IV DLBCL patients treated with standard immunochemotherapy. MTV was derived using fixed SUV (≥2.5, ≥4.0), relative (>41% SUVmax), and adaptive (liver-to-background) thresholds. Baseline MTV metrics significantly correlated with 3-year overall survival (OS3) in univariate analysis in overall cohort, with MTV41 showing the strongest association (HR: 1.27; p = 0.003). MTV25 and MTV41 remained significant in the stage 4 patient subgroup. However, in multivariate analysis, no MTV metric independently predicted OS3 when adjusted for the International Prognostic Index (IPI), which remained the dominant predictor (HR: 1.95; p < 0.0001). ROC analysis confirmed superior AUC for IPI (0.76) over PET-based metrics (0.64–0.69). Predictive models integrating IPI with PET metrics were robust but failed to improve prognostic accuracy beyond IPI alone. Although PET-derived MTV metrics provide prognostic value in univariate analysis, threshold selection has minimal impact, and their added value is limited when combined with IPI, reinforcing its role as the most reliable survival predictor in DLBCL.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".