The importance of imaging strategies for pre-clinical and clinical in vivo distribution of oncolytic viruses
Bibliographic record
Abstract
Oncolytic viruses (OVs) are an emergent and unique therapy for cancer patients. Similar to chemo- and radiation therapy, OV can lyse (kill) cancer cell directly. In general, the advantages of OVs over other treatments are primarily: a higher safety profile (as shown by less adverse effects), ability to replicate, transgene(s) delivery, and stimulation of a host's immune system against cancer. The latter has prompted successful use of OVs with other immunotherapeutic strategies in a synergistic manner. In spite of extended testing in pre-clinical and clinical setting, using biologically derived therapeutics like virus always raises potential concerns about safety (replication at non-intended locations) and bio-availability of the product. Recent advent in in vivo imaging techniques dramatically improves the convenience of use, quality of pictures, and amount of information acquired. Easy assessing of safety/localization of the biotherapeutics like OVs became a new potential weapon in the physician's arsenal to improve treatment outcome. Given that OVs are typically replicating, in vivo imaging can also track virus replication and persistence as well as precisely mapping tumor tissues presence. This review discusses the importance of imaging in vivo in evaluating OV efficacy, as well as currently available tools and techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".