Any regression of tumor (ART) within 12 weeks versus RECIST 1.1 response category as an intermediate endpoint to assess the activity of immune checkpoint inhibitors (ICIs) for metastatic urothelial carcinoma (mUC).
Bibliographic record
Abstract
473 Background: Many ICIs are approved to treat mUC following platinum-based chemo and as 1st-line therapy for selected patients (pts). While RECIST 1.1 responses are generally durable and associated with prolonged survival, the benefit of ICIs extends beyond this group of pts. Stable disease (SD) consists of a heterogeneous population of pts with both increase and decrease in tumor size and is confounded by the impact of pre-ICI disease pace. We hypothesized that in the setting of ICIs, any regression of tumor (ART) within 12 weeks may capture early benefit and correlate with survival more comprehensively than RECIST 1.1. Methods: mUC pts who received an ICI following platinum-based chemo at DFCI were eligible for analysis. Pts were required to have tumor size changes, RECIST 1.1 response by week 12 and survival data available. Demographics and prognostic factors were collected. Descriptive stats were calculated, and univariable Cox proportional hazards regression analysis was conducted to examine the prognostic effect of ART and RECIST 1.1 with overall survival (OS). Results: 104 pts were evaluable. The median age was 66 (range 34-89). 71% were male. The numbers of pts with ART and RECIST1.1 partial response (PR) were 45 (43.3%) and 32 (30.1%), respectively. Univariable analyses identified an association between ART and RECIST 1.1 response with OS (p<0.001). The 1-year OS (95% CI) for ART vs. no ART was 83.6 % (68.7, 91.8) and 35.9 % (23.1, 48.8), while the 1-year OS (95% CI) for RECIST 1.1 response vs. no response was 81.3% (62.9, 91.1) and 45.6% (32.9, 57.4), respectively. RECIST 1.1 category was not significantly associated with OS (p-value=0.68) after adjusting for ART; however, statistically, ART associated with OS (p=0.002) after adjusting for RECIST 1.1 category. The modest size of this cohort is a limitation. Conclusions: ART within 12 weeks is identified early and is robustly associated with OS in pts with mUC receiving post-platinum ICIs. ART may serve as a more optimal intermediate endpoint for survival compared to RECIST 1.1 in the setting of ICIs. Evaluating this endpoint in other malignancies is warranted.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".