RECIST (Response Evaluation Criteria in Solid Tumors) applied to response in lymphoma
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".