Treatment planning using tailored and standard cylindrical light diffusers for photodynamic therapy of the prostate
Bibliographic record
Abstract
Interstitial photodynamic therapy (PDT) has seen a rebirth, partially prompted by the development of photosensitizers with longer absorption wavelengths that enable the treatment of larger tissue volumes. Here, we study whether using diffusers with customizable longitudinal emission profiles, rather than conventional ones with flat emission profiles, improves our ability to conform the light dose to the prostate. We present a modified Cimmino linear feasibility algorithm to solve the treatment planning problem, which improves upon previous algorithms by (1) correctly minimizing the cost function that penalizes deviations from the prescribed light dose, and (2) regularizing the inverse problem. Based on this algorithm, treatment plans were obtained under a variety of light delivery scenarios using 5-15 standard or tailored diffusers. The sensitivity of the resulting light dose distributions to uncertainties in the optical properties, and the placement of diffusers was also studied. We find that tailored diffusers only marginally outperform conventional ones in terms of prostate coverage and rectal sparing. Furthermore, it is shown that small perturbations in optical properties can lead to large changes in the light dose distribution, but that those changes can be largely corrected with a simple light dose re-normalization. Finally, we find that prostate coverage is only minimally affected by small changes in diffuser placement. Our results suggest that prostate PDT is not likely to benefit from the use of tailored diffusers. Other locations with more complex geometries might see a better improvement.
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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 | 0.001 |
| 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) | 0.001 | 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".