Double reading for gross tumor volume assessment in radiotherapy planning
Bibliographic record
Abstract
Background/Objective: The precise definition of the gross tumor volume (GTV) that takes into account intra- and interobserver variability is necessary for high-precision radiotherapy (RT) techniques. The purpose of this study was to demonstrate the practical GTV assessment by a “double reading” approach. Methods: Pretreatment magnetic resonance (MR) imaging, including the post-contrast 3D magnetization-prepared rapid-gradient echo (MP-RAGE) sequence (section thickness 1.0 mm) was performed on a 3T superconducting imager in 50 patients with glioblastoma. MR images were transferred to a RT planning system (RTPS) that provides many opportunities for GTV contouring, e.g., at diagnosis, surgical navigation, and RT deliberations. Independent 2 observers preliminarily contoured the GTV on MR images. After planning-CT scanning, CT images with a 1.0 mm slice interval were transferred to the RTPS, registered with the diagnostic images, and then the preliminarily-contoured strictures were copied onto the CT images and used for GTV assessment. The practical GTV on the planning CT was determined by integrating the interpretations and adding information on postoperative changes. The interobserver variability in GTV contouring was assessed by Bland-Altman analysis and the concordance index. Results: There was substantial interobserver variability in GTV contouring (95% limits of agreement: -29.4%, 16.8%). The mean interobserver concordance rate for the GTV was 82.1% (range 56.5-91.2%). The practical GTVs were significantly larger than the preliminarily-contoured GTVs by both observers ( p < 0.01). Conclusions: Considering interobserver variability, “double reading” is necessary for practical GTV assessment. This approach for volume assessment may facilitate the standardization of treatments, not only of RT but also of surgery and chemotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".