THE PROGNOSTIC IMPACT OF CLINICAL FACTORS AND IMMUNOARCHITECTURAL PATTERNS FOR NODULAR LYMPHOCYTE‐PREDOMINANT HODGKIN LYMPHOMA: AN INTERNATIONAL STUDY BY GLOW
Notice bibliographique
Résumé
Introduction: Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) is a rare entity. Studies evaluating outcomes for patients of all ages with scoring of immunoarchitectural patterns (IAPs) are needed to inform optimal management. We sought to perform an international multicenter study of pediatric and adult patients with all stages of NLPHL to develop a prognostic model and assess the impact of IAP. Methods: Thirty-seven centers participated in the Global nLPHL One Working Group (GLOW) to retrospectively identify cases of NLPHL diagnosed from 1992 to 2021. Pathology was reviewed at individual centers with scoring of IAPs when available. For analysis, the presence of any variant IAP pattern (C-F) in biopsies were scored as variant cases. We measured progression-free survival (PFS), overall survival (OS), transformation rate, and lymphoma-specific death rate. We performed uni- and multivariable (MVA) Cox regression stratified by management type to select factors for inclusion in the lymphocyte-predominant international prognostic score (LP-IPS). Time-dependent ROC modeling was used to test model performance with bootstrapping for internal validation. Analyses were two-tailed and significant with p < 0.05 (R version 4.2.2). Results: We identified 2193 patients with a median age of 37 years (quartiles: 2–23, >23–37, >37–51, >51). Median follow up was 6.3 years (IQR = 3.5–10.8). Most patients were male (74.8%) and had stage I-II (73.3%) disease. A minority had B-symptoms (9.9%) or splenic involvement (5.1%) at presentation. IAP data was available for 916 patients (41%), of which 73.8% were pattern A/B, 8.5% C, 9.0% D, 7.3% E, and 1.4% F. Upfront management included: chemotherapy alone (32.0%), combined modality therapy (30.9%), radiotherapy alone (24.0%), observation after excision (4.6%), rituximab alone (4.0%), active surveillance (3.4%), and rituximab and radiotherapy (1.1%). PFS, OS, transformation, and lymphoma-specific death rates at 10 years were 71.1%, 91.7%, 4.9%, and 3.2% respectively. Individual IAPs were not significantly associated with PFS or OS on MVA, but pattern E was significantly associated with higher risk of transformation on MVA (HR = 1.81, p = 9.2e-3). Based on our MVA, model AUC statistics, and similar HR sizes, we developed the LP-IPS which assigns 1 point each for age ≥45, stage III–IV, Hb < 10.5 g/dL, or splenic involvement. Increasing LP-IPS was significantly associated with worse PFS (HR = 1.53), OS (HR = 2.34), lymphoma specific death (HR = 2.67), and higher rates of transformation (HR = 1.53) per risk point (p < 0.05). Keywords: diagnostic and prognostic biomarkers, Hodgkin lymphoma Conflicts of interests pertinent to the abstract K. J. Savage Employment or leadership position: Beigene Consultant or advisory role: Celgene, Seagen, BMS, Merck, Gilead, Astra Zeneca, Janssen, Abbvie Honoraria: BMS, Merck, Gilead, Astra Zeneca, Janssen, Abbvie Other remuneration: DSMC: Regeneron F. Vega Research funding: CRISP Therapeutics, Allogene, and Geron corporation Other remuneration: supported by a R01CA222918 from the National Cancer Institute A. Prica Honoraria: Astra-Zeneca, Abbvie, Kite Gilead D. Talaulikar Honoraria: Roche, Janssen, Beigene, MSL, EUSA, CSL, Amgen Research funding: Roche, Janssen, Beigene, MSL, EUSA, CSL, Amgen D. A. Eichenauer Honoraria: Takeda and Sanofi-Genzyme
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».