Interstitial Doppler Optical Coherence Tomography as a Local Tumor Necrosis Predictor in Photodynamic Therapy of Prostatic Carcinoma: An<i>In vivo</i>Study
Bibliographic record
Abstract
We have tested the feasibility of real-time localized blood flow measurements, obtained with interstitial (IS) Doppler optical coherence tomography (DOCT), to predict photodynamic therapy (PDT)-induced tumor necrosis deep within solid Dunning rat prostate tumors. IS-DOCT was used to quantify the PDT-induced microvascular shutdown rate in s.c. Dunning prostate tumors (n=28). Photofrin (12.5 mg/kg) was administered 20 to 24 hours before tumor irradiation, with 635 nm surface irradiance of 8 to 133 mWcm(-2) for 25 minutes. High frequency ultrasound and calipers were used to measure the thickness of the skin covering the tumor and the location of the echogenic IS probe within it. A two-layer Monte Carlo model was used to calculate subsurface fluence rates within the IS-DOCT region of interest (ROI). Treatment efficacy was estimated by percent tumor necrosis within the ROI, as quantified by H&E staining, and correlated to the measured microvascular shutdown rate during PDT treatment. IS-DOCT measured significant PDT-induced vascular shutdown within the ROI in all tumors. A strong relationship (R2=0.723) exists between the percent tumor necrosis at 24 hours posttreatment and the vascular shutdown rate: slower shutdown corresponded to higher treatment efficacy, i.e., more necrosis. Controls (needle+light, no drug, n=3) showed minimal microvascular changes or necrosis (4%+/-1%). This study has correlated a biological end point with a direct and localized measurement of PDT-induced microvascular changes, suggesting a potential clinical role of on-line, real-time microvascular monitoring for optimizing treatment efficacy in individual patients.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".