Longitudinal tumor hypoxia imaging with [18F]FAZA-PET provides early prediction of nanoliposomal irinotecan (nal-IRI) treatment activity
Bibliographic record
Abstract
BACKGROUND: Non-invasive measurement of tumor hypoxia has demonstrated potential for the evaluation of disease progression, as well as prediction and assessment of treatment outcome. [(18)F]fluoroazomycin arabinoside (FAZA) positron emission tomography (PET) has been identified as a robust method for quantification of hypoxia both preclinically and clinically. The goal of this investigation was to evaluate the feasibility and value of repeated FAZA-PET imaging to quantify hypoxia in tumors that received multi-dose chemotherapy. METHODS: FAZA-PET imaging was conducted over a 21-day period in a mouse xenograft model of HT-29 human colorectal carcinoma, following multi-dose chemotherapy treatment with irinotecan (CPT-11) or nanoliposomal irinotecan (nal-IRI, MM-398). RESULTS: Tumors treated with 10 mg/kg nal-IRI maintained significantly lower levels of hypoxia and smaller hypoxic fractions compared to tumors that received 50 mg/kg CPT-11. Specifically, differences in FAZA uptake were detectable 9 days before any significant differences in tumor volume were observed between the treatment groups. CONCLUSIONS: These findings highlight the potential use of FAZA-PET as an early marker of treatment response following multi-dose chemotherapy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".