Inherently Multimodal Nanoparticle-Driven Tracking and Real-Time Delineation of Orthotopic Prostate Tumors and Micrometastases
Bibliographic record
Abstract
Prostate cancer is the most common cancer among men and the second cause of male cancer-related deaths. There are currently three critical needs in prostate cancer imaging to personalize cancer treatment: (1) accurate intraprostatic imaging for multiple foci and extra-capsular extent; (2) monitoring local and systemic treatment response and predicting recurrence; and (3) more sensitive imaging of occult prostate cancer bone metastases. Recently, our lab developed porphysomes, inherently multimodal, all-organic nanoparticles with flexible and robust radiochemistry. Herein, we validate the first in vivo application of (64)Cu-porphysomes in clinically relevant orthotopic prostate and bony metastatic cancer models. We demonstrate clear multimodal delineation of orthotopic tumors on both the macro- and the microscopic scales (using both PET and fluorescence) and sensitively detected small bony metastases (<2 mm). The unique and multifaceted properties of porphysomes offers a promising all-in-one prostate cancer imaging agent for tumor detection and treatment response/recurrence monitoring using both radionuclide- and photonic-based strategies.
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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) | 0.000 | 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 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".