The use of spatial dose gradients and probability density function to evaluate the effect of internal organ motion for prostate IMRT treatment planning
Bibliographic record
Abstract
The aim of this study is to investigate the effects of internal organ motion on IMRT treatment planning of prostate patients using a spatial dose gradient and probability density function. Spatial dose distributions were generated from a Pinnacle3 planning system using a co-planar, five-field intensity modulated radiation therapy (IMRT) technique. Five plans were created for each patient using equally spaced beams but shifting the angular displacement of the beam by 15 degree increments. Dose profiles taken through the isocentre in anterior-posterior (A-P), right-left (R-L) and superior-inferior (S-I) directions for IMRT plans were analysed by exporting RTOG file data from Pinnacle. The convolution of the 'static' dose distribution D0(x, y, z) and probability density function (PDF), denoted as P(x, y, z), was used to analyse the combined effect of repositioning error and internal organ motion. Organ motion leads to an enlarged beam penumbra. The amount of percentage mean dose deviation (PMDD) depends on the dose gradient and organ motion probability density function. Organ motion dose sensitivity was defined by the rate of change in PMDD with standard deviation of motion PDF and was found to increase with the maximum dose gradient in anterior, posterior, left and right directions. Due to common inferior and superior field borders of the field segments, the sharpest dose gradient will occur in the inferior or both superior and inferior penumbrae. Thus, prostate motion in the S-I direction produces the highest dose difference. The PMDD is within 2.5% when standard deviation is less than 5 mm, but the PMDD is over 2.5% in the inferior direction when standard deviation is higher than 5 mm in the inferior direction. Verification of prostate organ motion in the inferior directions is essential. The margin of the planning target volume (PTV) significantly impacts on the confidence of tumour control probability (TCP) and level of normal tissue complication probability (NTCP). Smaller margins help to reduce the dose to normal tissues, but may compromise the dose coverage of the PTV. Lower rectal NTCP can be achieved by either a smaller margin or a steeper dose gradient between PTV and rectum. With the same DVH control points, the rectum has lower complication in the seven-beam technique used in this study because of the steeper dose gradient between the target volume and rectum. The relationship between dose gradient and rectal complication can be used to evaluate IMRT treatment planning. The dose gradient analysis is a powerful tool to improve IMRT treatment plans and can be used for QA checking of treatment plans for prostate patients.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".