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Outcomes of Nodular Lymphocyte Predominant Hodgkin Lymphoma (NLPHL) Vs. Classical Hodgkin Lymphoma (cHL) at Princess Margaret Cancer Centre

2015· article· en· W2533737381 on OpenAlexaffabout
Jeffery Ames, Manjula Maganti, Bethany Monteith, David Hodgson, Vishal Kukreti, John Kuruvilla, Anca Prica, Richard Tsang, A. Sun, Mary Gospodarowicz, Melania Pintilie, Michael Crump

Bibliographic record

VenueBlood · 2015
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreKingston General HospitalQueen's University
Fundersnot available
KeywordsMedicineInternal medicineLogistic regressionProportional hazards modelStage (stratigraphy)OncologyLymphomaCancerCancer registryClassical Hodgkin lymphomaCohortHodgkin lymphomaBiology

Abstract

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Abstract Background: Treatment of nodular lymphocyte predominant Hodgkin lymphoma (NLPHL) is controversial. While management generally follows that of classical HL (cHL), NLPHL demonstrates many biologic differences from cHL, and such patients (pts) are often excluded from prospective trials of novel therapies. The purpose of this study was to describe the characteristics and treatment outcomes of pts with NLPHL at the Princess Margaret Cancer Centre (PM) compared to those with cHL, with both groups managed following the same treatment algorithms according to stage at presentation. Methods: We identified 820 pts registered in our Lymphoma database from 1999 to 2013 who had primary treatment at PM; 50 had NLPHL and 770 had cHL. The outcomes of the two groups were compared utilizing the whole cohort as well as a 1:3 propensity score-matched subcohort. Propensity scores were calculated based on a logistic regression model with the probability of belonging to the NLPHL group as dependent variable. Pts were matched based on age, gender, treatment received, stage, extranodal disease and follow-up time. A competing risks approach was used to estimate the probability of relapse and Gray's test and Fine and Gray model were used to study significance between groups. A Cox regression model and the log-rank test were used to compare disease free survival (DFS). Results: Median age at diagnosis of pts with NLPHL was 38 years (range 12-73) and 28 years (4-88) for those with cHL (p=0.006); 20% of NLPHL pts were female versus 44% with cHL (p=0.008). Similar proportions of pts with NLPHL and cHL presented with stage I/II lymphoma (84% vs 73%, p=0.083) but extranodal (E) disease was less common in NLPHL (4% v 25%, p=0.006). Treatment received: NLPHL: radiation (IFRT) alone 26%, chemotherapy alone 20%, combined modality therapy (CMT) 54%; CHL: IFRT alone 2%; chemotherapy alone 24%; CMT 74%. Chemotherapy consisted of doxorubicin, bleomycin, vinblastine, dacarbazine (ABVD). The median follow-up time (range) was 7.7 (1.5-15.2) years in the NLPHL group versus 5.2 (0.03-15.9) years in the cHL group. There was no difference in DFS or relapse rate between pts who were diagnosed with cHL and NLPHL (p >0.7). Pts with stage III/IV NLPHL had the poorest outcome with the highest rate of relapse at 5 years (53.1%) compared to other groups (23.4%, 12.4% and 2.8%), and the lowest DFS at 5 years (47% vs. 97%, 85% and 74%, p<0.001). Pts with stage III/IV NLPHL were 2.9 times more likely to experience relapse than pts with stage III/IV cHL (p-0.03 (95% CI: 1.1-7.5)). Among the 50 pts with NLPHL, 47 could be matched with pts with cHL (n=126, Table 1). The response to treatment was similar between the matched pt groups, with 44 (94%) pts achieving CR/CRu/PR in the NLPHL group and 118 (94%) with cHL. As was seen for the whole cohort, no difference in the probability of relapse or DFS between the matched NLPHL and cHL pts (relapse at 5y:11.2% v 21.4%, DFS at 5y: 89 % vs. 75% p=NS); however, the hazard ratio for relapse for pts with stage III/IV NLPHL vs cHL was 3.0 (95% CI 0.83-10.9, p=0.09); relapse probability by subtype and stage is shown in figure 1. DFS at 5 years: NLPHL stage I/II 97%, stage III/IV 47%; cHL stage I/II 78%, stage III/IV 58% ; hazard ratio for DFS for pts with stage III/IV NLPHL vs cHL 2.0 (95% CI 0.53-7.6, p=0.31); Four pts with NLPHL and 1 with cHL developed a second lymphoma (4 DLBCL, 1 plasmablastic). Conclusion: Within the limits of relatively small patient numbers, the prognosis of pts with early stage NLPHL is excellent when treated with CMT approaches used for cHL, or IFRT alone, while pts with stage III/IV disease appear to have a high rate of recurrence with ABVD; alternative regimens should be considered in these patients. Table 1. Characteristics of matched cohorts Covariate CHL (N=126)N (%) NLPHL (N=47)N (%) Age at Diagnosis (yrs) Median (range) 36 (12-73) 36 (4-86) Gender % Female Male 29 (23.0) 97 (77.0) 10 (21.3) 37 (78.7) Treatment Chemotherapy CMT IFRT 25 (19.8) 85 (67.5) 16 (12.7) 10 (21.3) 27 (57.4) 10 (21.3) Stage I/II III/IV 108 (85.7) 18 (14.3) 39 (83.0) 8 (17.0) Extranodal N Y 119(94.4) 7(5.6) 45 (95.7) 2 (4.3) Response to Treatment CR/CRu/PR NR/SD 118 (93.7) 8 (6.3) 44 (93.6) 3 (6.4) Status Alive Death 110 16 45 2 Cause of Death HL or treatment toxicity Other/unknown alive 6 9 110 1 1 45 Competing risk (Relapse) No Relapse Relapse (incl PD) Death without Relapse 94 25 7 38 9 - Figure 1. Relapse probability according to stage, histology Figure 1. Relapse probability according to stage, histology Disclosures Kukreti: Roche: Honoraria; Lundbeck: Honoraria; Amgen: Honoraria; Celgene: Honoraria; Janssen Ortho: Honoraria. Kuruvilla:Seattle Genetics: Honoraria, Research Funding; Karyopharm: Honoraria, Research Funding; Roche Canada: Honoraria. Crump:Celgene: Honoraria; Sanofi: Honoraria; Seattle Genetics: Honoraria.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.021
GPT teacher head0.271
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations7
Published2015
Admission routes2
Has abstractyes

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