SU‐EE‐A3‐01: Evaluation of Image‐Guided Radiation Therapy (IGRT) Technology and Their Impact On the Outcome of Hypofractionated Prostate Cancer Treatments: A Radiobiological Analysis
Bibliographic record
Abstract
Purpose: To evaluate various image‐guidance technologies and their potential impact on the outcome of hypofractionated prostate cancer radiation therapy to mitigate against geometric uncertainties. Method and Materials: Five prostate cancer patients were analyzed. All patients were planned twice with an 18MV six‐field conformal technique with a 10 and 5 mm margin sizes, with various prescription doses (35 to 70 Gy) of equal late complications (assuming normal tissue α/β = 3 Gy). The various target localization techniques simulated were (1) laser alignment to external tattoo marks, (2) alignment to bony landmarks with daily portal images, (3) alignment to the clinical target volume (CTV) with daily CT imaging, and (4) the repeat of technique (3) with daily monitor unit updates to account for patient shape changes. The impact of uncertainty in the assumed α/β value for the prostate was also assessed. Results: For all treatment schedules simulated, technique (4) achieved the ideal condition most closely (i.e., ΔTCP = TCPtechnique − TCPplan ≈ 0 %), then followed by (3), (2) and (1). As the number of fractions decreased (i.e., increasingly hypofractionated), the ΔTCP generally decreased for all techniques. Because the hypofractionated schedules were designed to keep late complications constant, the NTCP values were also relatively constant for all treatment schedules. Generally, the average NTCP values were lower than the plan for all techniques. However, the most effective way to reduce NTCP was to reduce the margin size from 10 to 5 mm. Overall, the uncertainty in α/β values had a far more influence on the outcome of the hypofractionated treatment than would the geometric uncertainties. Conclusion: This study suggests that, although the impact of geometric uncertainties increases as the number of fractions decrease, the reduction in TCP due to the uncertainties does not significantly offset the expected gain in TCP by hypofractionation.
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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 | 0.001 |
| 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) | 0.001 | 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".