Early response assessment through multiparametric MRI based endpoints in a phase II multicenter study evaluating the efficacy of DPX-Survivac, intermittent low dose cyclophosphamide (CPA) and pembrolizumab combination study in subjects with solid tumors.
Bibliographic record
Abstract
e14245 Background: Accurate assessment of tumor response to immunotherapy is challenged by pseudoprogression that mimics true progression. Conventional imaging and RECIST assessment do not adequately distinguish between them given their inability to account for changes in the tumor microenvironment. DPX-Survivac is a novel T cell activating therapy that triggers immune responses against tumors expressing survivin and is being studied in this trial in combination with CPA and pembrolizumab in several solid tumors. Multiparametric MRI approaches - dynamic contrast-enhanced MRI and diffusion-weighted imaging MRI are useful for accurate assessment of structural, perfusion and vascular assessment of the lesion and may identify pseudoprogression and compare to the RECIST-based assessment. Methods: The study will enroll up to 226 evaluable subjects in 5 different cohorts: ovarian cancer, HCC, NSCLC, bladder cancer and MSI-H cancer. These subjects will undergo initial imaging 28 days prior to treatment, to be assessed based on RECIST 1.1, and a pre-treatment tumor biopsy for quantitation of survivin and PD-L1 expression and MSI analyses. Treatment for 35 cycles or until disease progression. All patients will have CT images for RECIST 1.1 and iRECIST assessment. A subset of subjects will undergo mpMRI to calculate advanced imaging biomarkers. Results: MRI, clinical and patient-reported outcomes will be analyzed. Conclusions: This study will provide important evidence on the utility of mpMRI + CT-based assessment of response to immunotherapy and use it as an adjunct to the CT-based RECIST criteria by providing insight on how tumor lesions are impacted by treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".