Bimodal Imaging Probes for Combined PET and OI: Recent Developments and Future Directions for Hybrid Agent Development
Bibliographic record
Abstract
Molecular imaging--and especially positron emission tomography (PET)--has gained increasing importance for diagnosis of various diseases and thus experiences an increasing dissemination. Therefore, there is also a growing demand for highly affine PET tracers specifically accumulating and visualizing target structures in the human body. Beyond the development of agents suitable for PET alone, recent tendencies aim at the synthesis of bimodal imaging probes applicable in PET as well as optical imaging (OI), as this combination of modalities can provide clinical advantages. PET, due to the high tissue penetration of the γ-radiation emitted by PET nuclides, allows a quantitative imaging able to identify and visualize tumors and metastases in the whole body. OI on the contrary visualizes photons exhibiting only a limited tissue penetration but enables the identification of tumor margins and infected lymph nodes during surgery without bearing a radiation burden for the surgeon. Thus, there is an emerging interest in bimodal agents for PET and OI in order to exploit the potential of both imaging techniques for the imaging and treatment of tumor diseases. This short review summarizes the available hybrid probes developed for dual PET and OI and discusses future directions for hybrid agent development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".