Towards Complete Tumor Resection: Novel Dual-Modality Probes for Improved Image-Guided Surgery of GRPR-Expressing Prostate Cancer
Bibliographic record
Abstract
Nuclear and optical dual-modality probes can be of great assistance in prostate cancer localization, providing the means for both preoperative nuclear imaging and intraoperative surgical guidance. We developed a series of probes based on the backbone of the established GRPR-targeting radiotracer NeoB. The inverse electron demand of the Diels–Alder reaction was used to integrate the sulfo-cyanine 5 dye. Indium-111 radiolabeling, stability studies and a competition binding assay were carried out. Pilot biodistribution and imaging studies were performed in PC-3 tumor-bearing mice, using the best two dual-labeled probes. The dual-modality probes were radiolabeled with a high yield (>92%), were proven to be hydrophilic and demonstrated high stability in mouse serum (>94% intact labeled ligand at 4 h). The binding affinity for the GRPR was in the nanomolar range (21.9–118.7 nM). SPECT/CT images at 2 h p.i. clearly visualized the tumor xenograft and biodistribution studies, after scanning confirmed the high tumor uptake (8.47 ± 0.46%ID/g and 6.90 ± 0.81%ID/g for probe [111In]In-12 and [111In]In-15, respectively). Receptor specificity was illustrated with blocking studies, and co-localization of the radioactive and fluorescent signal was verified by ex vivo fluorescent imaging. Although optimal tumor-to-blood and tumor-to-kidney ratios might not yet have been reached due to the prolonged blood circulation, our probes are promising candidates for the preoperative and intraoperative visualization of GRPR-positive prostate 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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".