Sci‐Thur PM Therapy‐08: Intra and Inter Observer Variability and Systematic Error in Prostate Delineation
Bibliographic record
Abstract
Individual sub‐processes of external beam radiation therapy can affect patient outcome. Specifically geometric uncertainties play a major role in tumor control and normal tissue complications. It is known that tumor definition varies among different observers but importantly, at least to our knowledge, few studies have reported their findings against a true gold standard, most relying on the population average as the reference value. This work uses the Visible Human Project® data for a set of cross‐indexed CT and anatomical photographic digital images of a human male cadaver. We use the anatomical images to define a gold standard for the prostate as defined by a group of experts. Six radiation oncologists then repeatedly (20 times over several weeks) defined the prostate alone on the corresponding CT images allowing us to quantify inter and intra observer random and importantly to measure systematic error. Individual and population means were tested for systematic error against the gold standard. We found that the observers routinely over estimated the prostate volume, and that our intra and inter observer variability is not significantly different than that reported in the literature. Significantly we report a systematic error in target definition, with the physicians failing to include all of the posterior prostate volume near the rectum. In contrast the observers rarely missed true prostate volume in the left, right or anterior quadrants, but we observe a systematic error in that normal tissue anterior to the prostate was routinely included as target tissue.
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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 | 0.013 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".