Validation of RECIST 1.1 for use with cytotoxic agents and targeted cancer agents (TCA): Results of a RECIST Working Group analysis of a 50 clinical trials pooled individual patient database.
Bibliographic record
Abstract
2534 Background: The Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 were derived from an international collaborative effort supported by data from clinical trials (16 studies, 9147 patients) on cytotoxic chemotherapy (CT), providing a standard tool for response assessment. RECIST’s role has been questioned for TCA. Using a pooled individual patient database (IPD) from clinical trials performed by industry and cooperative groups, we assessed whether modifications to RECIST are required to evaluate antitumor activity of TCA. Methods: Data were collected from phase 2 and 3 clinical trials testing TCA in solid tumors. To study the occurrence of mixed responses, the variability of response of lesions within patients was studied. Furthermore, response was correlated with survival through landmark analyses and time dependent Cox models. Results: Clinical data were obtained from 23,259 patients, mainly with lung (36%), colorectal (28%) or breast cancer (11%). 15,620 patients (67%) received a TCA, mainly transduction or angiogenesis inhibitors, either as single agent (37%) or combined with other TCAs (7%) or CT (56%); 28% received CT only and 5% best supportive care or placebo. Within-patient variability reduced as the number of lesions used for response assessment increased, and did so similarly for TCAs (+/- CT) and CT. Mixed responses seemed to occur similarly across these treatment categories as well. Landmark analyses showed improving overall survival by % tumor shrinkage and a clear distinction between the effect of tumor shrinkage and progressive disease (PD) according to RECIST 1.1. This was confirmed by time dependent analysis. In addition target lesion growth showed no marked improvement in overall survival prediction over and above the other components of RECIST 1.1 PD (new lesions, non-target PD), regardless of treatment (TCA, CT or both) received. Similar results were seen focusing on major tumor types and classes of TCA. Conclusions: Using a large IPD dataset we demonstrated that RECIST 1.1 performs equally well for response assessment of TCA as for CT. No modifications are required.
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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 | 0.255 | 0.278 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".