Dosimetric impact of image-guided 3D conformal radiation therapy of prostate cancer
Bibliographic record
Abstract
The goal of this work is to quantify the impact of image-guided conformal radiation therapy (CRT) on the dose distribution by correcting patient setup uncertainty and inter-fraction tumour motion. This was a retrospective analysis that used five randomly selected prostate cancer patients that underwent approximately 15 computed tomography (CT) scans during their radiation treatment course. The beam arrangement from the treatment plan was imported into each repeat CT study and the dose distribution was recalculated for the new beam setups. Various setup scenarios were then compared to assess the impact of image guidance on radiation treatment precision. These included (1) daily alignment to skin markers, thus representing a conventional beam setup without image guidance, (2) alignment to bony anatomy for correction of daily patient setup error, thus representing on-line portal image guidance, and (3) alignment to the 'CTV of the day' for correction of inter-fraction tumour motion, thus representing on-line CT or ultrasound image guidance. Treatment scenarios (1) and (3) were repeated with a reduced CTV to PTV margin, where the former represents a treatment using small margins without daily image guidance. Daily realignment of the treatment beams to the prostate showed an average increase in minimum tumour dose of 1.5 Gy, in all cases where tumour 'geographic miss' without image guidance was apparent. However, normal tissue sparing did not improve unless the PTV margin was reduced. Daily realignment to the tumour combined with reducing the margin size by a factor of 2 resulted in an average escalation in tumour dose of 9.0 Gy for all five static plans. However, the prescription dose could be escalated by 13.8 Gy when accounting for changes in anatomy by accumulating daily doses using nonlinear image registration techniques. These results provide quantitative information on the effectiveness of image-guided radiation treatment of prostate cancer and demonstrate that the dosimetric impact is patient dependent.
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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".