Monitoring early tumor response to drug therapy with diffuse optical tomography
Bibliographic record
Abstract
Although anti-angiogenic agents have shown promise as cancer therapeutics, their efficacy varies between tumor types and individual patients. Providing patient-specific metrics through rapid noninvasive imaging can help tailor drug treatment by optimizing dosages, timing of drug cycles, and duration of therapy-thereby reducing toxicity and cost and improving patient outcome. Diffuse optical tomography (DOT) is a noninvasive three-dimensional imaging modality that has been shown to capture physiologic changes in tumors through visualization of oxygenated, deoxygenated, and total hemoglobin concentrations, using non-ionizing radiation with near-infrared light. We employed a small animal model to ascertain if tumor response to bevacizumab (BV), an anti-angiogenic agent that targets vascular endothelial growth factor (VEGF), could be detected at early time points using DOT. We detected a significant decrease in total hemoglobin levels as soon as one day after BV treatment in responder xenograft tumors (SK-NEP-1), but not in SK-NEP-1 control tumors or in non-responder control or BV-treated NGP tumors. These results are confirmed by magnetic resonance imaging T2 relaxometry and lectin perfusion studies. Noninvasive DOT imaging may allow for earlier and more effective control of anti-angiogenic therapy.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".