Characteristics and outcomes of extranodal NK/t-cell lymphoma (ENKL): A North American (NA) multi-institutional experience.
Bibliographic record
Abstract
8060 Background: ENKL is a rare and aggressive subtype of peripheral T-cell lymphoma. Due to its geographic predilection there is a paucity of data on clinical experiences from non-Asian countries. The purpose of this study was to analyze characteristics and outcomes of patients (pts) with ENKL identified from major academic centers in NA. Methods: Pts with newly diagnosed CD56+ ENKL were retrospectively identified. Analyses included disease characteristics, ethnicity, therapy, and outcomes. Results: 115 pts (63.5% Caucasian, 20% Asian, 16.5% other) were identified across 10 centers diagnosed between 5/1990-5/2011 (Era 1: pre-2000, n=16; Era 2: 2000-2005, n=45; Era 3: post-2005, n=54). Median age was 52 years (19-88). 75 (65%) had stage I/II disease and were treated with combined modality therapy (CMT) n=48, chemotherapy (CT) n=14 or radiotherapy (RT) n=14. 40 pts had stage III/IV disease and were treated with CT (n=23), CMT (n=12) or RT (n=5). CT regimens used alone or in CMT were either anthracycline-based (n=68) or other (n=29). 63% of stage I/II pts and 40% with stage III/IV achieved complete remission (CR). 30 pts underwent a stem cell transplant (SCT); 14 in first CR and 16 at progression/relapse (autologous, n=21; allogeneic, n=9). Pts with stage I/II disease had a better progression-free survival (PFS) and overall survival (OS) compared with stage III/IV (12 vs 5.2 months (p=0.003) and 41.5 vs 8.9 months (p<0.0001), respectively). For all stages, treatment with CMT compared with CT or RT alone was also associated with better PFS and OS, 18.0 vs 3.9 months (p<0.0001), and 41.5 vs 10.2 months (p=0.002) respectively. Non-anthracycline-based regimens were associated with better PFS (p=0.001) and OS (p=0.045). No survival differences were seen between Asian and non-Asian pts. Conclusions: This series represents one of the largest experiences of ENKL in NA. Our data are consistent with Asian studies in: 1) majority of pts present with early stage disease; 2) overall poor outcome; 3) superiority of CMT and non-anthracycline regimens. Advances in understanding biology and international collaborative efforts are required to improve outcome in this rare entity.
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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 | 0.000 | 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".