Reliability of contour-based volume calculation for radiosurgery
Bibliographic record
Abstract
OBJECT: Determining accurate target volume is critical for both prescribing and evaluating stereotactic radiosurgery (SRS) treatments. The aim of this study was to determine the reliability of contour-based volume calculations made by current major SRS platforms. METHODS: Spheres ranging in diameter from 6.4 to 38.2 mm were scanned and then delineated on imaging studies. Contour data sets were subsequently exported to 6 SRS treatment-planning platforms for volume calculations and comparisons. This procedure was repeated for the case of a patient with 12 metastatic lesions distributed throughout the brain. Both the phantom and patient datasets were exported to a stand-alone workstation for an independent volume-calculation analysis using a series of 10 algorithms that included approaches such as slice stacking, surface meshing, point-cloud filling, and so forth. RESULTS: Contour data-rendered volumes exhibited large variations across the current SRS platforms investigated for both the phantom (-3.6% to 22%) and patient case (1.0%-10.2%). The majority of the clinical SRS systems and algorithms overestimated the volumes of the spheres, compared with their known physical volumes. An independent algorithm analysis found a similar trend in variability, and large variations were typically associated with small objects whose volumes were < 0.4 cm(3) and with those objects located near the end-slice of the scan limits. CONCLUSIONS: Significant variations in volume calculation were observed based on data obtained from the SRS systems that were investigated. This observation highlights the need for strict quality assurance and benchmarking efforts when commissioning SRS systems for clinical use and, moreover, when conducting multiinstitutional cross-SRS platform clinical studies.
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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 | 0.012 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".