SU‐E‐T‐885: A Planning Comparison of Dynamic Conformal Arc (DCA), Static Non‐ Coplanar Intensity Modulated Radiotherapy (NCP‐IMRT), Volumetric Modulated Arc Therapy (RapidArc), Robotic Radiosurgery (Cyberknife), and Helical Tomotherapy (HI‐ART TomoTherapy) for SRS
Bibliographic record
Abstract
Purpose: This work compares all linac‐based SRS treatment techniques currently available for single lesion cranial SRS. This study includes the planning comparison of dynamic conformal arc (DCA), static non‐coplanar intensity modulated radiotherapy (NCP‐IMRT), volumetric modulated arc therapy (RapidArc), robotic radiosurgery (Cyberknife), and helical tomotherapy (HI‐ART TomoTherapy) for cranial SRS. Methods: Thirteen target volumes with range 0.23 to 20.76 cc were retrospectively selected and transferred to a CT scan of a phantom designed for end‐to‐end SRS QA (Lucy phantom, Standard Imaging). Plans were developed using, iPlan TPS (v4.1, BrainLAB) for DCA (4 arcs) and NCP‐IMRT (16 beams), meanwhile the Eclipse TPS (v8.6, Varian Medical Systems) was used for the RapidArc (4 arcs) technique. Multiplan TPS (v3.5, Accuray) and TomoTherapy HI‐ART TPS (v3.1.4.23) was used for Cyberknife and TomoTherapy respectively. All plans were evaluated using four criteria, (1) Paddickˈs Conformity Index (CI), (2) Paddickˈs Gradient Index (GI), (3) Homogeneity Index and (4) Wagnerˈs Conformity/Gradient Index (CGI). Results: The average Paddick conformity index, CI was 0.64, 0.72, 0.76, 0.78, and 0.65 for the DCA, NCP‐IMRT, RapidArc, Cyberknife and Tomotherapy techniques respectively.The average Paddick gradient index, GI was 3.3, 3.6, 4.2, 4.4, and 4.9 for the DCA, NCP‐IMRT, RapidArc, Cyberknife and Tomotherapy techniques respectively. The average Wagnerˈs CGI was 71.6, 74.5, 72.2, 71.37, and 62.18 for the DCA, NCP‐IMRT, RapidArc, Cyberknife and Tomotherapy techniques respectively. IMRT‐based techniques and robotic radiosurgery showed better CI and CGI, whereas DCA showed the best dose fall off followed by NCP‐IMRT Conclusions: All methods were able to produce comparable plans for most of the targets tested. More importantly, it is suggested that for future planning studies the plan criteria must be explicit in their goals. In particular, the gradient index should be specified along with the desired dose prescription and conformality.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".