Transrectal ultrasound based prostate volume determination: Is the frustum algorithm more accurate than planimetry?
Bibliographic record
Abstract
PURPOSE: To compare reconstructed volumes calculated via planimetry and frustum algorithms in the context of stepped transrectal ultrasound (US) imaging, and to estimate the reconstruction error for prostate volumes. METHODS: Prostate contours for 40 permanent implant patients were delineated on magnetic resonance (MR) and transrectal US images by a radiation oncologist. Simulated images of ellipsoid and truncated cone geometrical objects were constructed to determine volume calculation accuracy. Simulation results were used to deduce the algorithm-associated error made when calculating transrectal US prostate volumes. RESULTS: For imaging without deliberate slice positioning, planimetry reconstruction was mostly accurate while the frustum algorithm underestimated the volume. The discrepancy was mostly due to the end slice reconstruction. For slice positioning that reflected US image acquisition, planimetry overestimated by half the superior slice volume on average while frustum underestimated by half the inferior slice volume. The estimated algorithm errors for prostate contours were 4% and -3%, respectively. CONCLUSIONS: The planimetry and frustum algorithms offer different interpretations for reconstruction and yield systematic differences in calculated volumes. Both algorithms introduce bias into transrectal US prostate volume determinations that may have clinical implications, planimetry overestimating and frustum underestimating the volume.
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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 | 0.008 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".