Dosimetry recommendations for NMIBC: a simulation and in vivo study (Conference Presentation)
Bibliographic record
Abstract
Recurrent Non-Muscle Invasive Bladder Cancer (NMIBC) is a diffuse disease, and patients have failed standard BCG therapy face prophylactic cystectomy. PDT fell out of favour due to its variable outcome, and high morbidity. To overcome PDT associate toxicity to the bladder’s muscle layer, the use of shorter wavelength and instillation of the photosensitizer were suggested. While either approach was shown to improve the outcome in animal models they have not previously combined in human studies. Additionally, the effects of highly variable tissue optical properties of the bladder and its shape have not been studied. Here, we present surface dose histograms derived from light propagation simulation in 6 human bladders using CT images for anatomical detail and the FullMonte software package. The ability of a single light sensor versus 3 or 12 light sensors to measure the average irradiance on the bladder surface was evaluated as a function of the bladder wall’s tissue optical properties. Results show that the irradiance in non-spherical bladders can vary over an order of magnitude, but the irradiance histograms are affected little by displacement of the emitter inside the bladder void. As the surface area monitored by a single sensor depends strongly on the bladder shape, the responsivity of a single sensor to the average bladder irradiance can vary equally. Twelve light sensors monitor the entire bladder surface almost complete and hence their average responsivity is constant to the average irradiance on the bladder largely independent of shape. The dependency of the sensor’s response on the tissue optical properties is also lower.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".