SU-FF-T-396: TCP and NTCP Variation with the Percentage of Prostate Treatment Fractions Delivered Under Image Guidance
Bibliographic record
Abstract
Purpose: In image guided radiotherapy, a treatment course for prostate cancer often consists of a mix of initial IGRT and conventional 3DCRT/IMRT fractions. This study examines how the number of IGRT fractions impacts the TCP and rectal NTCP. Method and Material: We simulated a standard six-field prostate XRT technique consisting of a total of 76 Gy over 38 daily fractions. Dose distribution within the pelvic region for a simulated patient were calculated using Theraplan® plus for a margin of 5 and 10 mm corresponding IGRT and 3DCRT respectively. The dose distributions for two plans were then exported to Matlab 6.5 for radiobiological simulation. In the simulation, the prostate shifts were sampled from our shift database using Monte Carlo technique. Overall dose distribution to CTV and Rectum was obtained by summing fractional dose voxels over 38 fractions. A Poisson model coupled with linear-quadratic model was used to calculate TCP while Lyman model was used to calculate the rectal complication. A moderate value of 3.1 Gy for α/β was applied for both prostate and rectum. Results: 2000 treatment courses were simulated for possible number of IGRT fractions from 0 to 38. TCP and NTCP were subsequently calculated. Our simulation suggests that rectal complication is continually improved with the increasing IGRT fractions. However, TCP is optimized only when 7–10 IGRT fractions are applied as an adaptive measure. Conclusion: The number of IGRT fractions is a variable which can impact TCP, NTCP, and throughput. This simulation shows that the use of 7 imaged/repositioned fractions as an IGRT technique can safely allow dose escalation of 4 Gy to the prostate while imaging and repositioning all fractions can reduce rectal complication by up to 50% compared to treatment without image guidance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".