Internal fiducial markers can assist dose escalation in treatment of prostate cancer: result of organ motion simulations
Bibliographic record
Abstract
Use of internal fiducial markers and electronic portal imaging (EPI) to realign patients has been shown to significantly reduce positioning uncertainties in prostate radiation treatment. This creates the possibility of improving the treatment by decreasing the planning target volume (PTV) margin added to the clinical target volume (CTV), which in turn may allow dose escalation. Conformal treatment plans for three prostate cancer patients were evaluated by using different PTV margins with dose prescription of 70 Gy/35 fr initially. Two beam arrangements, 4-field-box (4FB) and 4-field-oblique (4FO), were used. Then, two dose escalation schemes, 74 Gy and 78 Gy, with tighter PTV margins, were chosen from the first simulation and were tested. A Monte Carlo model was developed to simulate the daily geometric uncertainty and calculate the dose to each organ. After the whole treatment, dose-volume histograms were produced and tumour control probability, prostate equivalent uniform dose and the effective dose to critical organs were calculated. By comparing these radiobiological metrics, optimized dose escalation schemes were found. The results show that using internal fiducial markers and EPI, the prescription dose can be escalated to 78 Gy/39 fr with a 4 mm PTV margin. Based on the available dose-response data for intermediate risk prostate patients, this is estimated to result in a 20% increase of local control and significantly reduced rectal complications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".