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RECIST (Response Evaluation Criteria in Solid Tumors) applied to response in lymphoma

2004· article· en· W2339464282 on OpenAlexaffabout
Sarit Assouline, E. Eisenhauer

Bibliographic record

VenueJournal of Clinical Oncology · 2004
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsOntario Institute for Cancer Research
Fundersnot available
KeywordsMedicineResponse Evaluation Criteria in Solid TumorsNuclear medicineClinical trialCruCancerTarget lesionInternal medicinePhases of clinical research

Abstract

fetched live from OpenAlex

6606 Background: RECIST utilizing unidimensional tumor measures are widely accepted for assessing response in solid tumor clinical trials. We have investigated whether these criteria can be applied to response assessment in lymphoma. However, to do so, complete response (CR) criteria were modified to include the following: a) baseline node(s) >15 mm must regress to ≤15 mm; b) nodes 10–15 mm must regress to normal; and c) enlarged spleen or liver is non-measurable and must return to normal size. Further, CR unconfirmed (CRu) by IWC was considered PR by RECIST. Methods:Using RECIST with these modifications, we determined clinical response in three phase II lymphoma trials of the National Cancer Institute of Canada Clinical Trials Group and compared results with response as determined by the International Working Criteria (IWC) (Cheson B, JCO 1999). Kappa estimated agreement of response (CR+PR+CRu) and non-response (PR+SD) between RECIST and IWC. Pearson's coefficient estimated correlation between changes in uni- and bidimensional measurements for responses based on these measurements alone. Results: 115 pts were evaluable, 92 with non-Hodgkin's and 23 with Hodgkin's lymphoma. By IWC, 14 achieved CR, 2 CRu, 32 PR, 52 SD, 15 PD, for an overall response (OR) of 42%. By RECIST, there were 14 CR, 39 PR, 47 SD, 15 PD, and OR 46%. κ=0.81 (95% CI: 0.70, 0.91), Pearson's r=0.81(95% CI: 0.71, 0.88) for changes in uni- and bidimensional measurements (n=75) for the two instruments. Conclusions: After some adaptations, applying RECIST to lymphoma yields near identical OR as IWC. There is high agreement between responses determined using uni- and bidimensional measurements. The benefits of RECIST over IWC include: familiarity among cancer trialists, simplicity of measuring unidimensional disease and providing follow-up measurements only for nodes >15 mm at baseline, and ability to include patients whose lymphoma is extranodal only. RECIST do not include CRu as a response category but OR is the likely estimate of interest in phase II clinical trials of novel agents. We therefore propose adapted RECIST be further studied for assessment of response in lymphoma. No significant financial relationships to disclose.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.183
metaresearch head score (Gemma)0.767
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1830.767
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.715
GPT teacher head0.698
Teacher spread0.016 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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
Published2004
Admission routes2
Has abstractyes

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