Prostate IMRT: Two-dimensional model of rectal NTCP employing the variability of rectal motion and rectum wall thickness
Bibliographic record
Abstract
Background: In order to improve the evaluaƟon of possible rectal toxicity based on the rectal normal Ɵ ssue complicaƟon probability (NTCP), we consider the fracƟonal dependence of the NTCP on the wall thickness (tW) and rectal displacement (RM). Materials and Methods: The two-dimensional NTCP model (NTCP2D) was developed using radiotherapy plans of ten randomly selected paƟents with prostate cancer. The clinical rectal structures were subsƟtuted with rectal walls of cylindrical shape. To simulate full, parƟally-full and empty state of the rectum, three tW were generated under the condiƟons of same length of the rectum and same volume of the rectal wall. A threshold iso-line, NTCPTR, was used to split the NTCP2D field into areas: a lower risk area and a higher risk area for rectal toxicity. Two factors are introduced to help with the esƟmaƟon of NTCP: a volume factor k1 which is the raƟo between the volumes of the rectal wall and the intersecƟon of the rectal wall with the planning target volume; and a probability factor k2, which is the raƟo between the area of low risk to the enƟre area of the NTCP2D. Results: A correlaƟon > 0.9 between factors k1 and k2 was found. Conclusion: The NTCP2D field and the raƟos k1 and k2 can be used as a paƟent-specific parameters to evaluate the probability of rectal toxicity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".