Magnetic resonance imaging for prostate bed radiotherapy planning: An inter‐ and intra‐observer variability study
Bibliographic record
Abstract
INTRODUCTION: We assessed the inter- and intra-observer variability in contouring the prostate bed for radiation therapy planning using MRI compared with computed tomography (CT). METHODS: We selected 15 patients with prior radical prostatectomy. All had CT and MRI simulation for planning purposes. Image fusions were done between CT and MRI. Three radiation oncologists with several years of experience in treating prostate cancer contoured the prostate bed first on CT and then on MRI. Before contouring, each radiation oncologist had to review the Radiation Therapy Oncology Group guidelines for postoperative external beam radiotherapy. The agreement between volumes was calculated using the Dice similarity coefficient (DSC). Analysis was done using the Matlab software. The DSC was compared using non-parametric statistical tests. RESULTS: Contouring on CT alone showed a statistically significant (P = 0.001) higher similarity between observers with a mean DSC of 0.76 (standard deviation ± 0.05) compared with contouring on MRI with a mean of 0.66 (standard deviation ± 0.05). Mean intra-observer variability between CT and MRI was 0.68, 0.75 and 0.78 for the three observers. The clinical target volume was 19-74% larger on CT than on MRI. The intra-observer difference in clinical target volume between CT and MRI was statistically significant in two observers and non-significant in the third one (P = 0.09). CONCLUSIONS: We found less inter-observer variability when contouring on CT than on MRI. Radiation Therapy Oncology Group contouring guidelines are based on anatomical landmarks readily visible on CT. These landmarks are more inter-observer dependent on MRI. Therefore, present contouring guidelines might not be applicable to MRI planning.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".