SU‐D‐BRC‐01: Multi‐Fraction Dose Distributions for Image‐Guided IMRT of Prostate: Impact on TCP and NTCP
Bibliographic record
Abstract
Purpose: IMRT of the prostate consists of multiple stages of treatment planning and dose delivery, each with inherent anatomical uncertainties. Differential filling of the bladder and rectum can displace the prostate during a course of treatment. The purpose of this study was to assess the effectiveness of adapting to these anatomical changes using various CT image‐guided adaptive (IGART) strategies. Methods: Our computer model is based on the Philips Pinnacle treatment planning system. A multi‐fraction simulation of 5‐field IMRT (76 Gy/35 fractions) yields the daily dose accumulation in individual tissue elements. Using megavoltage CT studies of 13 prostate cases, cumulated dose distributions are mapped onto the reference treatment plan. Total dose‐volume histograms are then processed to estimate the changes in tumor control probability (TCP) and normal tissue complication probability (NTCP) for various IGART scenarios. Results: Retargeting of the prostate generally maintains the intended TCP (typically 0.9 versus 0.8 with No Image Guidance) but is often associated with an enhanced risk of rectal toxicity (NTCP rises to 0.05 when Image Guidance is applied). This effect is due mainly to a systematic anterior shift caused by sag in the treatment couch (average 10mm). Without image‐guidance, this offset goes uncorrected, resulting in poorer coverage of the target with consistent avoidance of rectal exposure. Conclusions: Geometric repositioning without dose re‐planning is sufficient to maintain the intended TCP. IGART corrects for sag in the treatment couch and this results in greater risk of rectal toxicity, compared with no IGART. Smaller PTV margins could offset this effect, emphasizing the need for integrated image guided and intensity modulated delivery for optimal results. In the extreme, daily IMRT re‐planning based on in‐room imaging could exploit maximum benefits of this technology but this would require significantly more computing resources and streamlining of procedures at the treatment console. Research funding was provided by the Canadian Health Research Institutes and the Ontario Research and Development Challenge Fund (OCITS Project), with Philips Medical Systems as an industry partner.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".