Bladder Filling Variation in Cancer Cervix Patients Treated by Image Guided Radiation Therapy
Bibliographic record
Abstract
Variable bladder filling during radiotherapy for carcinoma cervix significantly affects the position of target volumes to be treated. This directly impacts the precision with which advanced radiotherapy plans are delivered using specific margins to target volumes. The aim is to study mean bladder volumes and mean range of bladder motion in all three dimensions in patients of carcinoma cervix treated with Image guided radiation therapy (IGRT), and compare them to the baseline planning Computed tomography (CT) scans. For this, a total number of 25 patients/ 150 CT scans, i.e. 25 planning scan and 125 CBCT (cone-beam CT) were analyzed. The bladder volumes and bladder wall dimension were analyzed using offline views of CBCT imaging, conducted weekly for cervix cancer patients, thus adding considerable understanding to the bladder wall motions. The mean bladder volume for all 25 patients was 263.41 cc with standard deviation of 110.174 cc. When mean CBCT bladder volumes of each patient were compared to their respective planning CT bladder volumes, P was found to be insignificant (p=0.25), showing consistent bladder filling or reproducibility during treatment. The mean ± standard deviation of bladder’s transverse, anteroposterior and supero-inferior dimension was 9.55 ± 0.923 cm; 6.92 ± 2.387 cm and 7.22 ± 0.967 cm. The mean supero-inferior bladder diameter had significant variation from that of the planning CT (7.84 ± 2.749 cm vs 6.92 ± 2.387) with p = 0.018. This study shows that more liberal margins should be considered in supero-inferior dimensions and considerable lesser margins could be given on the lateral sides, as there is less displacement in transverse diameter.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".