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Indolent Non-Hodgkin Lymphoma: Skeletal Muscle Density Predicts Overall Survival With Rituximab Based Chemotherapy

2013· article· en· W348899769 on OpenAlexaffabout
Michael P. Chu, Jessica Lieffers, Andrew R. Belch, Neil Chua, Amélie Fontaine, Randeep Sangha, A. Robert Turner, Sunita Ghosh, Michael Sawyer

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineSarcopeniaInternal medicineDiffuse large B-cell lymphomaLymphomaOncologyChemotherapy regimenSkeletal muscleRituximabChemotherapyInternational Prognostic Index

Abstract

fetched live from OpenAlex

Abstract Introduction Sarcopenia is an adverse risk factor for solid organ malignancies. Recent evidence suggests sarcopenia confers a poor prognosis in diffuse large B-cell lymphoma, but further study is needed to evaluate its role in other Non-Hodgkin Lymphoma (NHL) subtypes. Low skeletal muscle density (SMD) has also been identified as a risk factor for poor prognosis; it is more prognostic than sarcopenia in renal cell carcinoma and melanoma. Low SMD is hypothesized to be a marker of inflammation that suggests more active disease. 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 follicular lymphoma (FL). Methods FL patients from 2004-2009 who received rituximab-based chemotherapy at our institution were retrospectively reviewed. Aside from baseline information (stage, age, gender, height, weight, performance status, FL International Prognostic Index 1 score [FLIPI-1], chemotherapy regimen received), progression free survival (PFS) and overall survival (OS) was 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 145 FL patients were identified. Median age was 59 years (range 29-83 years), with a median FLIPI-1 score of 2, median stage III, 79 male, and 66 female. The majority of patients received R-CVP (rituximab, cyclophosphamide, vincristine, and prednisone) chemotherapy with a median 8 cycles received (range 1-8) and 87 patients given maintenance rituximab. Median PFS and OS were 44.7 and 56.8 months, respectively. Patients with sarcopenia failed to have significant differences in either PFS or OS. The PFS hazard ratio (HR) of 1.26 suggested a trend for poor outcomes in sarcopenic patients (p=0.17). A specific cut-off for sarcopenia was not identifiable. However, comparing patients with SMD below the median to those above yielded a PFS of 40.9 vs 49.7 months (HR 1.91; p=0.01), respectively. Significant differences in OS similarly occurred for below and above the median SMD with 52.8 vs 63.3 months (HR 2.61;p=0.01). A discernible cut-off parameter for SMD was identified at 36.61 HU. PFS for those with lower than this SMD was a more pronounced detriment at 39.3 vs 55.3 months (HR 2.76; p=0.0005), respectively. OS of 51.9 vs 64.7 months (HR 4.67; p=0.0001) was also more pronounced at levels below and above the SMD cut-off parameter, respectively. Multivariate analysis found OS (HR = 4.08; p=0.004) in favor of the higher SMD group and independent of FLIPI-1 or gender. Conclusions In FL, SMD is a strong prognostic marker independent of the FLIPI-1 scores while sarcopenia has less of a prognostic role. SMD can be used as an additional tool to stratify FL patients. Evaluation of SMD and its mechanistic link with inflammation requires further study. Disclosures: No relevant conflicts of interest to declare.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.253
Teacher spread0.240 · 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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Citations0
Published2013
Admission routes2
Has abstractyes

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