The use of phase sequence image sets to reconstruct the total volume occupied by a mobile lung tumor
Bibliographic record
Abstract
The use of phase sequence image (PSI) sets to reveal the total volume occupied by a mobile target is presented. Isocontrast composite clinical target volumes (CCTVs) may be constructed from PSI sets in order to reveal the total volume occupied by a mobile target during the course of its travel. The ability of the CCTV technique to properly account for target motion is demonstrated by comparison to contours of the true total volume occupied (TVO) for a number of experimental phantom geometries. Finally, using real patient data, the clinical utility of the CCTV technique to properly account for internal tumor motion while minimizing the volume of healthy lung tissue irradiated is assessed by comparison to the standard approach of applying safety margins. Results of the phantom study reveal that CCTV cross sections constructed at the 20% isocontrast level yield good agreement with the total cross sections (TXO) of mobile targets. These CCTVs conform well to the TVOs of the moving targets examined whereby the addition of small uniform margins ensures complete circumscription of the TVO with the inclusion of minimal amounts of surrounding external volumes. The CCTV technique is seen to be clearly superior to the common practice of the addition of safety margins to individual CTV contours in order to account for internal target motion. Margins required with the CCTV technique are eight to ten times smaller than those required with individual CTVs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".