Evaluation of clinical markup with CT simulation in whole brain radiation therapy
Bibliographic record
Abstract
2043 Background: Two types of clinical markup have been used for whole brain radiation therapy (WBRT), but the extent of dose coverage and sparing of the lens is unknown. This study is designed to evaluate clinical markup with CT simulation. Methods: CT simulation images of the brain from 20 random adult patients were included in this study. Two types of inferior field borders were generated to mimic the 2 types of clinical markup of WBRT: from superior orbital ridge (SOR) to tragus, and from SOR to inferior tragal notch (ITN). A field margin of 1 cm from contoured brain (along base of skull) was used in CT simulation, except near the orbital globe where 0.5 cm margin to eye shielding was used. Two opposed lateral fields with 6 MV photon were used to generate 3 WBRT plans in each patient: clinical markup with SOR to tragus, SOR to ITN, and CT simulation. Dose volume histograms of the brain and lens were generated. The primary end point was dose coverage of whole brain with clinical markup. The secondary end point was sparing of the lens. Results: Patients with clinical markup using SOR to tragus or SOR to ITN had significantly larger median brain volume receiving less than 95% of the prescribed dose, 37.0 mL and 7.8 mL (respectively) compared to CT simulation 0 mL (P < 0.001). All patients with SOR to tragus and most patients (95%) with SOR to ITN clinical markup had underdosing in the regions of inferior frontal lobe, temporal lobe and posterior fossa. There was no significant difference in the dose to the lens between CT simulation and clinical markup using SOR to tragus (P = 0.18) or SOR to ITN (P = 0.90). Conclusions: Whole brain radiation therapy with clinical markup using either SOR to tragus or SOR to ITN results in underdosing at the inferior frontal lobe, temporal lobe and posterior fossa. Sparing of the lens is adequately achieved with clinical markup. No significant financial relationships to disclose.
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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 | 0.003 | 0.013 |
| 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 | 0.001 | 0.001 |
| 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".