Magnetic resonance imaging correlated with the histopathological effect of Pd‐bacteriopheophorbide (Tookad) photodynamic therapy on the normal canine prostate gland
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: To determine the optimal magnetic resonance imaging (MRI) methodology to assess photodynamic therapy (PDT)-induced histopathological responses in the prostate. STUDY DESIGN/MATERIALS AND METHODS: Laparotomy was performed in five healthy dogs. Cylindrical diffuser was placed in the prostates to deliver light of 50-300 J/cm at 150 mW/cm and 763 nm to activate IV-injected Tookad (1 mg/kg b.w.). Fast spin echo (FSE) T2-weighted, post-contrast-enhanced T1-(CE-T1) and diffusion weighted images (DWI) were obtained pre- and 2 days, 7 days, and 1 month post-PDT. Radiological-histopathological correlation was performed at 7 days (n = 4) and 1 month (n = 1) after PDT. A qualitative assessment of signal changes and apparent diffusion coefficient (ADC) mapping was performed. RESULTS: At 2 or 7 days post-PDT, there was good spatial correlation between PDT-induced hemorrhagic necrosis and unenhanced regions on CE-T1 images. There was a rapidly and persistently enhancing rim corresponding to edema and inflammation. FSE T2 and DWI showed altered signal but did not clearly define necrosis in all cases. At 1 month, it was hard to correlate MR images to histopathologic changes as they represented a mixture of necrosis and developing fibrosis, which led to a mixed signal intensity and less demarcated contrast enhancement. CONCLUSIONS: At 7 days after PDT, gadolinium DTPA contrast-enhanced MRI is superior to DWI and T2 imaging in assessing the boundary of Tookad PDT-induced tissue necrosis in the normal canine prostate.
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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 | 0.000 |
| 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".