Outcome prediction by extranodal involvement, IPI, R‐IPI, and NCCN‐IPI in the PET/CT and rituximab era: A <scp>D</scp>anish–<scp>C</scp>anadian study of 443 patients with diffuse‐large <scp>B</scp>‐cell lymphoma
Bibliographic record
Abstract
18F-fluorodeoxyglucose PET/CT (PET/CT) is the current state-of-the-art in the staging of diffuse large B-cell lymphoma (DLBCL) and has a high sensitivity for extranodal involvement. Therefore, reassessment of extranodal involvement and the current prognostic indices in the PET/CT era is warranted. We screened patients with newly diagnosed DLBCL seen at the academic centers of Aalborg, Copenhagen, and British Columbia for eligibility. Patients that had been staged with PET/CT and treated with R-CHOP(-like) 1(st) line treatment were retrospectively included. In total 443 patients met the inclusion criteria. With a median follow-up of 2.4 years, the 3-year overall (OS) and progression-free survival (PFS) were 73% and 69%, respectively. The Ann Arbor classification had no prognostic impact in itself with the exception of stage IV disease (HR 2.14 for PFS, P<0.01). Extranodal involvement was associated with a worse outcome in general, and in particular for patients with involvement of >2 extranodal sites, including HR 7.81 (P < 0.001) for PFS for >3 sites. Bone/bone marrow involvement was the most commonly involved extranodal site identified by PET/CT (29%) and was associated with an inferior PFS and OS. The IPI, R-IPI, and NCCN-IPI were predictive of PFS and OS, and the two latter could identify a very good prognostic subgroup with 3-year PFS and OS of 100%. PET/CT-ascertained extranodal involvement in DLBCL is common and involvement of >2 extranodal sites is associated with a dismal outcome. The IPI, R-IPI, and NCCN-IPI predict outcome with high accuracy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".