Dosimetric evaluation of daily rigid and nonrigid geometric correction strategies during on‐line image‐guided radiation therapy (IGRT) of prostate cancer
Bibliographic record
Abstract
The purpose of this study is to evaluate a geometric image guidance strategy that simultaneously correct for various inter-fractional rigid and nonrigid geometric uncertainties in an on-line environment, using field shape corrections (called the "MU-MLC" technique). The effectiveness of this strategy was compared with two other simpler on-line image guidance strategies that are more commonly used in the clinic. To this end, five prostate cancer patients, with at least 15 treatment CT studies each, were analyzed. The prescription dose was set to the maximum dose that did not violate the rectum and bladder dose-volume constraints, and hence, was unique to each patient. Deformable image registration and dose-tracking was performed on each CT image to obtain the cumulative treatment dose distributions. From this, maximum, minimum, and mean dose, as well as generalized equivalent uniform dose (gEUD) were calculated for each image guidance strategy. As expected, some dosimetric differences in the clinical target volume (CTV) were observed between the three image guidance strategies investigated. For example, up to +/-2% discrepancy in prostate minimum dose were observed among the techniques. Of them, only the "MU -MLC" technique did not reduce the prostate minimum dose for all patients (i.e., > or = 100%). However, the differences were clinically not significant to indicate the preference of one strategy over another, when using a uniform 5 mm margin size. For the organ-at-risks (OARs), the large rectum sparing effect (< or =5.7 Gy, gEUD) and bladder overdosing effect (< or = 16 Gy, gEUD) were observed. This was likely due to the use of bladder contrast during CT simulation studies which was not done during the treatment CT studies. Therefore, ultimately, strategies to maintain relatively constant rectum and bladder volumes, throughout the treatment course, are required to minimize this effect. In conclusion, the results here suggest that simple translational corrections based on three-dimensional (3D) images is adequate to maintain target coverage, for margin sizes at least as large as 5 mm. In addition, due to large fluctuations in OAR volumes, innovative image guidance strategies are needed to minimize dose and maintain consistent sparing during the whole course of radiation therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".