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Fibrosis in Nodular Sclerosis Hodgkin Lymphoma Is Predictive of a Residual Mass Following Therapy.

2009· article· en· W2569899586 on OpenAlexaff
Alanna J. Church, Nasim Shabazi, David P. LeBrun, Tara Baetz

Bibliographic record

VenueBlood · 2009
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineBiopsyLymphomaNodular sclerosisRadiologyStage (stratigraphy)FibrosisPositron emission tomographyPathologyHodgkin lymphoma

Abstract

fetched live from OpenAlex

Abstract Abstract 4630 Persistence of a mass after first-line treatment is a common problem in nodular sclerosis Hodgkin lymphoma (NSHL). Up to 64% of patients demonstrate residual abnormalities on computed tomography (CT) after therapy, but only 42% of those patients will relapse on follow-up. This is primarily caused by the inability of CT to distinguish viable tumor tissue from fibrosis. Clinicians are faced with the dilemma of whether to pursue second-line treatment for a mass that may be simply scar tissue. The ability to predict which patients are at higher risk for residual mass following curative treatment can aid in the planning of follow-up imaging modalities such as positron emission tomography (PET) scanning which can map out metabolically active tissue (i.e. tumor versus fibrosis) and the need for biopsy of a residual mass. This study was designed to test the hypothesis that the presence of abundant fibrosis in the initial biopsy predicts the presence of residual, post-therapy masses composed primarily of fibrotic tissue. Subjects were consecutive NSHL patients from the years 1996 to 2007 identified from our institution based on the availability of histology slides from the initial diagnostic biopsy, clinical follow-up data, and the results of post-treatment imaging investigations. The initial biopsies were reviewed by a lymphoma pathologist and resident without knowledge of the residual mass status. The proportion of the tissue consisting of fibrous material was graded as a percentage of the total biopsy tissue. The clinical charts were reviewed for baseline patient characteristics, cancer stage and the presence of a residual mass on CT scan 6 months after treatment. Of the 47 subjects included in the study, 25 had residual masses and 22 had none. Patients with increased fibrosis on initial biopsy were significantly more likely to have a residual mass after initial therapy (p=0.028). The degree of fibrosis was independent of gender, stage, and Hasenclever score. Degree of fibrosis was the only factor that was predictive of the presence of a residual mass. Of the 16 patients with residual masses with follow-up Gallium imaging, the result of the scan was more likely to be negative (indicating that the mass is not metabolically active) for patients with a high grade of initial fibrosis (p=0.148). Taken together, these results suggest that patients with increased fibrosis on their initial NSHL biopsy are more likely to have residual masses, but that these masses are less likely to be malignant. The results support the hypothesis that the degree of fibrosis at the initial NSHL biopsy is predictive of a post-treatment residual mass. These findings have potential implications for patient follow-up: clinicians whose patients have abundant fibrosis at initial biopsy may be reassured that a post-treatment residual mass is less likely to represent persistent malignancy and thus can be followed with functional imaging rather than pursuing unnecessary biopsies. Our results further reinforce the importance of functional imaging like PET, particularly in this patient population 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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.247
Teacher spread0.228 · 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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

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