SU‐DD‐A2‐03: Anisotropic Edema Modelling for Permanent Prostate Implants
Bibliographic record
Abstract
Purpose: To develop an edema model for application in permanent prostate implant dosimetry that reflects edema spatial anisotropy and time‐resolution behaviors observed in clinical MRI data. Method and Materials: The basic manifestation of edema was represented as a trio of variations in Cartesian components of the distance, ri(t); i=1:3, from a radioactive seed to a dose calculation point; specifically . Here (r0)i are distance components in the absence of edema, fi(t) edema time‐resolution functions, and αi quantifiers of the directional contributions to edema volume subject to the constraint . Serial MRI data from our institution for n=40 prostate implant patients is well characterized by the parameters , and the function ; t ; i=1:3. Hence these parameters and time‐resolution function were incorporated in the model. Next, the cumulative dose from a seed to a calculation point was expressed according to the TG‐43 formalism (using the anisotropy constant) as , where λ is the radionuclide decay constant and the other variables are defined as per TG‐43. The integrand was then expanded in even powers of |r⃗(t)|, and the resulting integral in each term of the expansion evaluated to yield either a closed‐form analytic function or a convergent series. Numerical evaluation of constituent terms in the dose expansion was done in MatLab to assess functional behavior. Results: The number of terms in the expansion of D(r⃗) are few, and all are well‐behaved numerically. For those terms involving convergent series, convergence is rapid and so only a small number of terms need to be evaluated. Based on these favorable properties, MatLab‐based dose calculation software is currently being developed to investigate applications to clinical dosimetry. Conclusion: A new edema model for permanent prostate implant dosimetry incorporating spatial anisotropy has been formulated. Clinical application is pending.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".