Sci-Thurs PM: Planning-05: Comparison of 4DCT Contouring on Exhalation + Inhalation Phases and Maximum Intensity Projection (MIP) for Lung Cancer
Bibliographic record
Abstract
4DCT has been widely used in radiation therapy planning to determine target motion and generate the internal target volume (ITV). However, contouring on all respiratory phases created by 4DCT may not be the most efficient way to generate ITV. To develop a practical contouring protocol of adequate accuracy, we compared in this study two available approaches: one is to combine the target contours from the maximum exhalation and inhalation phases only; the other is to contour on the derived Maximum Intensity Projection (MIP). In this study, our physicians contoured the target for 11 lung-cases (Stage Ia to IIIb), all scanned using 4DCT on a Philips 16-slice big bore. For the 2 of the 11 cases without the target abutting other structures, the combined contour from the exhale and inhale phases (ITV_ex+in) was entirely encompassed by the contour from MIP (ITV_MIP). However, 28% (6.3cc) of ITV_MIP was outside ITV_ex+in in one case. This underestimation of the target volume by ITV_ex+in might be partially due to the curved trajectory of the target motion. In the rest 9 cases, all with the target abutting other structures, 5 cases had 2.5∼6.8cc () of ITV_ex+in outside ITV_MIP. The underestimation of the target volume by ITV_MIP was due to portions of the target in one respiratory phase being shadowed in MIP by abutting structures from another phase. Our results indicate that for determining ITV in lung on 4DCT it is necessary to combine contours from all three sets including MIP, exhale and inhale phases.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".