Treatment Patterns and Outcomes in Adolescent and Young Adults with Nodular Lymphocyte Predominant Hodgkin Lymphoma: An Impact Cohort Population-Based Study
Bibliographic record
Abstract
Introduction: Nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) is a rare subtype of Hodgkin lymphoma distinct from classical HL (cHL). NLPHL treatment is not standardized. Adolescents and young adults (AYA) are a vulnerable subpopulation of cancer patients whose care is dispersed among both pediatric and adult institutions. We leveraged a population-based database of linked clinical and healthcare data to identify treatment patterns and long-term outcomes among younger AYA with NLPHL. Methods: The IMPACT Cohort comprises all Ontario, Canada AYA aged 15-21 years diagnosed with one of six common cancers (including HL) between 1992-2012. Detailed demographic, disease, treatment, and outcome data were collected through chart abstraction and validated by content experts. Linkage to population-based health administrative data identified additional cancer events (second malignant neoplasms [SMN], relapses, deaths). Demographic, disease, and treatment variables were compared between AYA with cHL vs. NLPHL, including treatment modalities received (surgery alone vs. radiation vs. chemotherapy vs. combined modality treatment [CMT]). Among AYA with NLPHL, treatment patterns were also compared between those treated at pediatric vs. adult centers. Event-free (EFS) and overall survival (OS) were compared between cHL and NLPHL patients using Kaplan-Meier estimates. Predictors of outcome among AYA with NLPHL were determined using multivariable Cox proportional hazard models. Events included disease progression, relapse, death, and SMN. The cumulative incidence of SMN and major cardiac events (determined through validated algorithms using healthcare data) was determined. Results: Of 1,014 AYA with HL, 54 (5.3%) had NLPHL. AYA with NLPHL were more likely to be male [42/54 (77.8%) vs. 458/960 (47.7%); p<0.001] and to have limited stage disease [33/54 (61.1%) vs. 397/960 (41.4%); p<0.01]. Compared to AYA with cHL, AYA with NLPHL were more likely to be treated with radiation only [16/54 (29.6%) vs. 58/960 (6.0%); p<0.001]. Among those who received radiation, the median dose administered was higher in AYA with NLPHL (35Gy vs. 30Gy; p=0.04). Conversely, among those who received chemotherapy, cumulative doses were lower in NLPHL. Of the 54 patients with NLPHL, 15 (27.8%) were treated at a pediatric center. No pediatric center patient received radiation only vs. 16 (41.0%) of adult center patients. Treatment consisting of surgery only was more common in pediatric centers (Table 1). Median radiation doses were also lower among pediatric centers (21Gy vs. 35Gy; p=0.007). Though cumulative doses of doxorubicin and bleomycin were lower at pediatric centers, the differences were not statistically significant. The 10- and 20-year EFS for AYA with NLPHL was 82.9%±5.2% and 77.4%±7.2% respectively, which did not significantly differ from cHL (p=0.52; Figure 1). The 20-year OS was 100.0% among AYA with NLPHL vs. 90.1%±1.2% among those with cHL (p=0.04). Among patients with NLPHL, only advanced stage (HR 4.9, 95CI 1.3-18.4; p=0.02) was significantly associated with inferior EFS in univariate analyses. In multivariable analyses neither stage nor treatment modality was significantly associated with EFS. OS models were not possible given the low number of deaths. The 15-year EFS and OS for AYA with NLPHL treated with surgery only were both 100%. The 25-year cumulative incidence of SMN among AYA with NLPHL was 19.3%±9.6%. These included cases of thyroid carcinoma, squamous cell carcinoma, and diffuse large B-cell lymphoma (N≤5 each). Though higher than the equivalent cumulative incidence among AYA with cHL (14.9%±3.0%), this difference was not statistically significant. There were no SMN among AYA with NLPHL treated with surgery only. There were no episodes of major cardiac events among AYA with NLPHL. Conclusions: Overall, AYA with NLPHL have an outstanding long-term survival. However, treatment patterns vary considerably. Resection alone was rare outside of pediatric institutions, though such patients had excellent outcomes, while radiation alone was common in adult institutions. Given substantial long-term burden of SMN, more widespread use of chemotherapy and radiation-sparing strategies for appropriate subsets of AYA with NLPHL is warranted. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".