Survival Analysis of Anaplastic Large Cell Lymphoma, Systemic and Cutaneous-Types: Report from the International T-Cell Lymphoma Project.
Bibliographic record
Abstract
Abstract The WHO (World Health) Organization Classification recognizes two distinct subtypes of anaplastic large cell lymphoma: Primary systemic and Primary cutaneous types, which have differences in immunophenotype, genetics, and clinical behavior. It is now known that approximately 60% of systemic ALCLs express the anaplastic lymphoma kinase (ALK) protein (ALK-pos) and have a significantly superior survival to ALK-neg cases. Since ALK-neg ALCL appear to have a prognosis similar to peripheral T-cell lymphoma unspecified (PTCL-U), it has been suggested that they should be classified as PTCL-U. Herein, we report the clinical features of newly diagnosed systemic and cutaneous ALCL from the International T-cell Lymphoma Study Group. Materials and Methods: 186 cases of ALCL were identified by the WHO disease definitions: systemic ALCL 163 (88%) (ALK-pos 91 (56%), 72(44%) ALK-neg), and 23 (17%) cutaneous ALCL (cut ALCL). The median age of ALK-pos, ALK-neg and cut ALCL was 32, 57.5 and 54, respectively. There was a male predominance for all subtypes. Most cases of systemic ALCL presented with stage III or IV disease (64% ALK-pos, 58% ALK-neg) and in contrast, 87% of cut ALCL had localized disease. The majority of patients with systemic ALCL were treated with CHOP-type chemotherapy. Most patients with cut ALCL (91%) received additional therapy: 13 (62%) CHOP-type chemotherapy, 11(52%) chemoradiation, 4 (19%) radiation alone. Results: The 5y failure free survival (FFS) and overall survival (OS) was superior for ALK-pos ALCL (70.5% and 58%) compared to ALK-neg ALCL (49% and 36%) (p=.022 and p=.014 for FFS and OS, respectively). Comparison of ALK-pos (n=16) and ALK-neg ALCL (n=23) patients with limited stage disease (defined as stage I or II, no B symptoms and non-bulky) failed to demonstrate a significant difference in FFS (p=.54) or OS (p=.21). Both ALK-pos and ALK-neg ALCL had a superior FFS (ALK-pos p< .001; ALK-neg p=.012) and OS (ALK-pos p<.001; ALK-neg p=.032) than PTCL-U. In contrast to PTCL-U, an apparent plateau was observed on the FFS curve for ALK-neg ALCL. For cut ALCL, the 5y FFS and OS was 90% and 57%, superior to systemic ALCL. The administration of chemotherapy did not appear to impact outcome in patients with cut ALCL (p=.64). Among the prognostic factors analyzed, the international prognostic index (IPI) was the most effective for defining risk categories in ALK-neg ALCL. For ALK-pos ALCL both the IPI and anemia (Hb < 11.0 g/L) were effective in risk-group stratification in multivariate analysis. Conclusions: Similar to prior reports, ALK-pos ALCL has a superior outcome to ALK-neg ALCL. For limited stage patients, this survival difference is not apparent, suggesting that a small subgroup of patients with ALK-neg ALCL may have a more favorable prognosis, similar to ALK-pos ALCL. The IPI is effective in both ALK-neg and ALK-pos ALCL at risk stratification. Finally, contrary to prior reports, ALK-neg ALCL patients appear to have a superior outcome to PTCL-U and an apparent plateau in the FFS curve. These results suggest that ALK-neg ALCL should still be distinguished from both ALK-pos ALCL and PTCL-U.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".