Are blood flow and blood volume predictors of localized photosensitizer accumulation in the brain?
Bibliographic record
Abstract
SIGNIFICANCE: The efficacy of photodynamic therapy (PDT) can be impacted by heterogeneous Photosensitizer (PS) accumulation. Our previous study indicated that neglecting spatial variations in photosensitizer (PS) accumulation during treatment planning can result in morbidity and treatment failure. Knowledge and incorporation of the PS distribution at the 1 mm³ scale in the treatment planning process can compensate for heterogeneous PS efficacy losses. Effects of vascular perfusion parameters in the brain and local PS concentration are investigated. AIM: Correlations between MRI-derived blood flow (BF), blood volume (BV), mean transit time (MTT), and quantitative Spatial Frequency Domain imaging (qSFDI) of the photosensitizer concentration [PS] in the tumour rim, core, and normal brain are investigated. METHOD: In-vivo MRI with continuous arterial spin labeling (CASL) and intravoxel incoherent motion (IVIM) provided BF, BV, and MTT in a rat glioma model for the tumour regions, normal brain, and spatially resolved within 1.5 mm of the tumour rim. Two photosensitizers were used: a small-molecule agent (Ce6) and a nanoparticle-based formulation (Porphysome). qSFDI provided spatially resolved [PS], which was co-registered with the MRI data to enable evaluation of the perfusion and [PS] correlation strength. RESULTS: The imaging techniques showed elevated BF and [PS] in the tumour rim and reduced BF and [PS] in the tumour core, but BV did not differ between the core and rim. No strong correlations between any perfusion parameter and ex-vivo [PS] were observed. A strong positive [PS] gradient was noted from the tumour's outer rim towards its centre. This spatial uptake trend was observed for both photosensitizers, with Porphysome showing a steeper gradient and higher overall accumulation. CONCLUSION: These findings highlight that MR perfusion metrics alone are insufficient to predict spatial [PS] in solid brain tumours for PDT pre-treatment planning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".