Sci‐Sat AM (2) Therapy‐02: Calcifications and tissue composition in post‐implant prostate dosimetry: a clinical study with Monte Carlo
Bibliographic record
Abstract
We developed an automated Monte Carlo (MC) multipurpose program to study different tissue configurations for post‐implant prostate brachytherapy dosimetry. We used our program for a clinical study on 18 cases. The usage of the DICOM‐RT protocol allows for an efficient transfer of data (Hounsfield Units [HU], seed positions, and organ contours) from the SPOT Pro system (version 3, Nucletron B.V., The Netherlands) to the MC program. A complete model was developed: for each voxel, the density is set by the HU and the elemental composition is set according to the radiation oncologist's contours. Between 180 and 320 different materials (prostate tissue, muscle, rectum tissue, bladder tissue, calcification, adipose tissue, and various mixtures) are utilized for each patient. The comparison between the TG‐43 based dosimetry and the complete MC simulation leads to average differences of 12±3 Gy for the D 90 CTV parameter. Three calcification scenarios were considered to finalize the MC model. When compared to the complete model, the first calcification scenario leads to an average decrease of 14 Gy on the D 90 CTV parameter while the second calcification scenario leads to an average increase of 6 Gy. The impact on organs at risk was also evaluated. The inclusion of prostate calcifications in the MC model is important but it is not possible to establish which scenario is the most realistic with traditional CT data. Research on dual‐energy CT is underway to facilitate the management of calcifications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".