RECIST 1.1 for Response Evaluation Apply Not Only to Chemotherapy-Treated Patients But Also to Targeted Cancer Agents: A Pooled Database Analysis
Bibliographic record
Abstract
PURPOSE: The mode of action of targeted cancer agents (TCAs) differs from classic chemotherapy, which leads to concerns about the role of RECIST in evaluating tumor response in trials with TCAs. We investigated the performance of RECIST using a pooled database from 50 clinical trials with at least one TCA. METHODS: We examined the impact of the number of target lesions (TLs) on within-patient variability of tumor response. The prognostic effect of TL response (at 12 weeks or on study on the basis of a maximum five TLs) on survival was studied through landmark and time-dependent Cox models adjusted for baseline tumor load, occurrence of new lesions, or unequivocal progression of nontarget disease. RESULTS: Data were obtained from 23,259 patients with cancer (36% lung, 28% colorectal, 11% breast, and 25% other); 15,620 received TCAs, predominantly transduction or angiogenesis inhibitors, as a single agent (37%), combined with other TCAs (7%), or as chemotherapy (56%); 28% received chemotherapy only; and 5% received best supportive care or placebo. A total of 17,222 patients contributed to the analyses. Within-patient variability decreased with increasing number of TLs, similarly for TCAs (with/without chemotherapy) and chemotherapy only. Mixed responses occurred proportionally in all treatment classes. Landmark analyses showed an ordinal relationship between percentage change from baseline to 12 weeks and overall survival, and demonstrated a clear distinction between tumor shrinkage and progressive disease according to RECIST. Time-dependent analysis showed no marked improvement in the ability to predict survival on the basis of TL tumor growth compared with nontarget progression or new lesion occurrence, regardless of treatment. Similar results were seen for major tumor types and different classes of TCAs. CONCLUSION: This work reinforces that RECIST version 1.1 perform well for response assessment of TCAs.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".