Preclinical <sup>89</sup>Zr Immuno-PET of High-Grade Serous Ovarian Cancer and Lymph Node Metastasis
Bibliographic record
Abstract
UNLABELLED: The elevation of cancer antigen 125 (CA125) levels in the serum of asymptomatic patients precedes the radiologic detection of high-grade serous ovarian cancer by at least 2 mo and the final clinical diagnosis by 5 mo. PET imaging of CA125 expression by ovarian cancer cells may enhance the evaluation of the extent of disease and provide a roadmap to surgery as well as detect recurrence and metastases. METHODS: (89)Zr-labeled mAb-B43.13 was synthesized to target CA125 and evaluated via PET imaging and biodistribution studies in mice bearing OVCAR3 human ovarian adenocarcinoma xenografts. Ex vivo analysis of tumors and lymph nodes was performed via autoradiography, histopathology, and immunohistochemistry. RESULTS: PET imaging using (89)Zr-DFO-mAb-B43.13 (DFO is desferrioxamine) clearly delineated CA125-positive OVCAR3 xenografts as early as 24 h after the administration of the radioimmunoconjugate. Biodistribution studies revealed accretion of (89)Zr-DFO-mAb-B43.13 in the OVCAR3 tumors, ultimately reaching 22.3 ± 6.3 percentage injected dose per gram (%ID/g) at 72 h after injection. Most interestingly, activity concentrations greater than 50 %ID/g were observed in the ipsilateral lymph nodes of the xenograft-bearing mice. Histopathologic analysis of the immuno-PET-positive lymph nodes revealed the presence of grossly metastasized ovarian cancer cells within the lymphoid tissues. In control experiments, only low-level, non-specific uptake of (89)Zr-labeled isotype IgG was observed in OVCAR3 tumors; similarly, low-activity concentrations of (89)Zr-DFO-mAb-B43.13 accumulated in CA125-negative SKOV3 tumors. CONCLUSION: Immuno-PET with (89)Zr-labeled mAb-B43.13 is a potential strategy for the noninvasive delineation of extent of disease and may add value in treatment planning and treatment monitoring of high-grade serous ovarian cancer.
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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) | 0.002 | 0.001 |
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".