Risk stratification in diffuse large B-cell lymphoma using lesion dissemination and metabolic tumor burden calculated from baseline PET/CT†
Bibliographic record
Abstract
BACKGROUND: We analyzed the prognostic value of a new baseline positron emission tomography (PET) parameter reflecting the spread of the disease, the largest distance between two lesions (Dmax). We tested its complementarity to metabolic tumor volume (MTV) in a large cohort of diffuse large B-cell lymphoma (DLBCL) patients from the REMARC trial (NCT01122472). PATIENTS AND METHODS: MTVs were defined using the 41% maximum standardized uptake value threshold. From the three-dimensional coordinates, the centroid of each lesion was automatically obtained and considered as the lesion location. The distances between all pairs were calculated. Dmax was obtained for each patient and normalized with the body surface area [standardized Dmax (SDmax)]. RESULTS: (n = 82) had a 4-year PFS and OS of 46% and 71%, respectively, against 77% and 87%, respectively, for patients with low SDmax. High SDmax and high MTV were independent prognostic factors of PFS (P = 0.0001 and P = 0.0010, respectively) and OS (P = 0.0028 and P = 0.0004, respectively). Combining MTV and SDmax yielded three risk groups with no (n = 109), one (n = 122) or two (n = 59) factors (P < 0.0001 for both PFS and OS). The 4-year PFS were 90%, 63%, 41%, respectively, and the 4-year OS were 95%, 79%, 66%, respectively. In addition, patients with at least two of the three factors including high SDmax, high MTV, Eastern Cooperative Oncology Group (ECOG) ≥2 had a higher number of central nervous system relapse (P = 0.017). CONCLUSIONS: SDmax is a simple feature that captures lymphoma dissemination, independent from MTV. These two PET metrics, SDmax and MTV, are complementary to characterize the disease, reflecting the tumor burden and its spread. This score appeared promising for DLBCL baseline risk stratification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".