Influence of body mass index and periprostatic fat on rectal dosimetry in permanent seed prostate brachytherapy
Bibliographic record
Abstract
PURPOSE: We examined the influence of body mass index (BMI) and body fat distribution on rectal dose in patients treated with permanent seed brachytherapy for localized prostate cancer. METHODS AND MATERIALS: We analyzed 213 patients treated with I125 seed brachytherapy for localized prostate cancer. BMI and rectal dosimetry data for all patients were available. Data on visceral and subcutaneous fat distribution at the level of the iliac crest (n = 140) as well as the distribution of periprostatic and subcutaneous fat at the symphysis pubis level were obtained (n = 117). Fat distribution was manually contoured on CT on day 30 after brachytherapy. The correlation between BMI, fat distribution and rectal dose (R100 (in cc), R150 (cc), D2 (Gy)) was analyzed using the Spearman correlation coefficient. Differences in rectal dose between tertiles of body fat distribution were calculated using nonparametric tests. RESULTS: Periprostatic adipose was only weakly correlated with BMI (r = 0.0.245, p = 0.008) and only weakly correlated with the other fat measurements (r = 0.31-0.37, p < 0.001). On the other hand, BMI was correlated with all other fat measurements (≥0.58, p < 0.001). All the other fat measurements were strongly correlated with each other (r = 0.5-0.87, p < 0.001). Patients with an R100 of >1.3 cc (23% of patients) had less visceral fat (p = 0.004), less subcutaneous fat at the level of the iliac crest (p = 0.046) and a lower BMI (26.8 kg/m2 vs. 28.5 kg/m2, p = 0.02) than patients with an R100 of <1.3 cc. Results were very similar when comparing an R100 of >1.0 cc (34% of patients) across the tertiles. None of the tested linear regression models were predictive (max 12%) of dose to the rectum. CONCLUSION: Dose to the rectum is dependent on BMI and body fat distribution. Periprostatic fat does not influence rectal dose. Dose to the rectum remains difficult to predict and depends on many factors, one of which is body fat distribution.
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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".