Aggressive Non-Hodgkin Lymphoma: Predictive Value Of Sarcopenia and Skeletal Muscle Density On Prognosis With Rituximab Containing Therapy
Notice bibliographique
Résumé
Abstract Introduction Sarcopenia is an established adverse risk factor for solid organ malignancies. Recent evidence suggests sarcopenia predicts a poor prognosis in elderly patients with diffuse large B-cell lymphoma (DLBCL) independent of Revised International Prognostic Index (R-IPI) scores. Because of the focus on an elderly population, it is difficult to generalize to the DLBCL population as a whole. Newer evidence suggests low skeletal muscle density (SMD) is a more significant indicator of poor prognosis in renal cell carcinoma and melanoma than sarcopenia. SMD can be approximated using computed tomography (CT) images and measuring muscle radiation attenuation in Hounsfield Units (HU). An average muscle SMD of <30 HU is considered to be poorly functioning muscle and has the appearance of ectopic fat production. This study examines sarcopenia and SMD in DLBCL. Methods DLBCL patients from 2004-2009 who received rituximab-based chemotherapy through our institution were retrospectively reviewed. Aside from baseline information (stage, age, gender, height, weight, performance status, R-IPI score, chemotherapy regimen and cycles received), progression free survival (PFS) and overall survival (OS) were collected as primary endpoints. Sarcopenia and SMD were calculated using Slice-o-Matic (Tomovision, Montreal Canada) with patients’ pre-treatment CT images. Skeletal muscle was defined as between -29 to 150 HU, intramuscular adipose tissue -190 to -30 HU; and visceral adipose tissue -150 to -50 HU. Skeletal muscle surface area and average radiation attenuation at the L3 vertebral body level were measured. Sarcopenia was pre-defined using skeletal muscle surface area cut-offs outlined in prior solid organ malignancy studies and from the elderly DLBCL study. Results We identified 224 DLBCL patients. Median age at diagnosis was 62 years (range 21-88 years), with 124 male, and 100 female. Median stage at diagnosis was III with a median IPI score of 3. The majority received R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone) with a median of 6 cycles (range 1-8). Median PFS and OS were 55.2 and 56.3 months, respectively. Patients with sarcopenia did not have a significant difference in either PFS or OS. In fact, the PFS hazard ratio (HR) of 0.70 would suggest sarcopenia as being protective but it was not statistically significant (p=0.19). Subgroup analysis of elderly DLBCL patients (defined as >70 years), found sarcopenia was protective for both PFS and OS yielding HRs of 0.24 and 0.45, respectively (p=0.002 and 0.05). A statistically insignificant PFS improvement by SMD was seen above and below the median SMD with 61.0 and 52.8 months, HR 1.28 (p=0.32), respectively. However, OS was significantly better in those above the median SMD at 65.5 vs 51.4 months, HR 2.02 (p=0.006). A cut-off point in SMD was noticeable at 26.63 HU where PFS was significantly worse in those that had lower SMD with 53.3 vs 56.3 months, HR 1.74 (p=0.03). OS was also significantly poorer with SMD lower than this cut-off, 51.9 vs 59.2 months, HR 1.92 (p=0.01), respectively. This difference though failed to maintain significance in multivariate analysis taking into consideration R-IPI and gender. Conclusions Contrary to recent evidence suggesting sarcopenia as a poor prognostic factor in elderly patients with DLBCL, our study demonstrates that sarcopenia may in fact be protective. Perhaps patients with lower lean body mass may be exposing their disease to relatively higher concentrations of chemoimmunotherapy. SMD is more prognostic than sarcopenia in DLBCL patients. While these findings suggest muscle mass and muscle quality play a strong role in the disease process, factors captured in the R-IPI score predict clinical course more strongly. Disclosures: No relevant conflicts of interest to declare.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 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) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».