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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".