The value of semiquantitative <scp>PET</scp> features and end‐of‐therapy <scp>PET</scp> in grade <scp>3B</scp> follicular lymphoma
Bibliographic record
Abstract
Summary Grade 3B follicular lymphoma (G3BFL) is a rare lymphoma thought to sit on a continuum between low‐grade FL and diffuse large B‐cell lymphoma (DLBCL). The prognostic impact of quantitative positron emission tomography (PET) metrics such as total metabolic tumour volume (TMTV), total lesion glycolysis (TLG), and maximum standard uptake value (SUVmax) have been extensively analysed in FL and DLBCL, but G3BFL data are lacking. Here, we describe PET outcomes and radiomic characteristics in 46 G3BFL cases uniformly treated with R‐CHOP (like) chemotherapy. Central semi‐automated PET TMTV, TLG, and SUVmax analyses, using MIM software, were correlated with clinical outcomes and compared with published results in low‐grade FL and DLBCL. In G3BFL, the end‐of‐treatment complete metabolic response was associated with improved progression‐free survival (PFS; p = 0.002) and overall survival (OS; p = 0.04). G3BFL median TLG (1455) and SUVmax (16.50) sit between published values for low‐grade FL (TLG: 1112, SUVmax: 11.3) and DLBCL (TLG: 3004, SUVmax: 24.35). No association between TMTV (>350 cm 3 ) and survival was seen (PFS: p = 0.24; OS: p = 0.40). High SUVmax (>19.2) and TLG (>2760) both conferred inferior PFS but not OS (PFS: SUVmax p = 0.004; TLG p = 0.05). These data support the routine incorporation of PET radiomics at baseline and treatment response for G3BFL.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".