Emerging Applications for Optically Enabled Intravital Microscopic Imaging in Radiobiology
Bibliographic record
Abstract
Radiation therapy is an effective cancer treatment used in over 50% of cancer patients. Preclinical research in radiobiology plays a major role in influencing the translation of radiotherapy-based treatment strategies into clinical practice. Studies have demonstrated that various components of tumors and their microenvironments, including vasculature, immune and stem cells, and stromal cells, can influence the response of solid tumors to radiation. Optically enabled imaging techniques used in experimental animal models of cancer offer a unique and powerful way to quantitatively track spatiotemporal changes in these tumor components in vivo at macro-, meso-, and microscopic resolutions following radiotherapy. In this review, we discuss the role of both well-established and emerging intravital microscopy techniques for studying tumors and their microenvironment in vivo, in response to irradiation. The development and application of new animal models, small animal microirradiation technologies, and multimodal optically enabled intravital microscopy techniques are emphasized within the framework of preclinical radiobiology research. We also comment on the potential influence that these newer imaging techniques may have on the clinical translation of new preclinical radiobiology discoveries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".