Comprehensive Quantitative Evaluation of Inter-observer Delineation Performance of MR-guided Delineation of Oropharyngeal Gross Tumor Volumes and High-risk Clinical Target Volumes: An R-IDEAL Stage 0 Prospective Study
Bibliographic record
Abstract
Purpose: Tumor and target volume manual delineation remains a challenging task in head and neck cancer radiotherapy. The purpose of this study is to conduct a multi-institutional evaluation of manual delineations of gross tumor volume (GTV), high-risk clinical target volume (CTV), parotids, and submandibular glands on treatment simulation MR scans of oropharyngeal cancer (OPC) patients. Methods: Pre-treatment T1-weighted (T1w), T1-weighted with Gadolinium contrast (T1w+C) and T2-weighted (T2w) MRI scans were retrospectively collected for 4 OPC patients under an IRB-approved protocol. The scans were provided to twenty-six radiation oncologists from seven international cancer centers who participated in this delineation study. In addition, clinical history and physical examination findings along with a medical photographic image and radiological results were provided. The contours were compared using overlap and distance metrics using both STAPLE and pair-wise comparisons. Lastly, participants completed a brief questionnaire to assess personal experience and CTV delineation institutional practices. Results: Large variability was measured between observer delineations for both GTVs and CTVs. The mean Dice Similarity Coefficient values across all case delineations for GTVp, GTVn, CTVp, and CTVn where 0.77, 0.67, 0.77, and 0.69, respectively, for STAPLE comparison and 0.67, 0.60, 0.67, and 0.58, respectively, for pair-wise analysis. Normal tissue contours were defined more consistently when considering overlap and distance metrics. The median radiation oncology clinical experience was 7 years and the median experience delineating on MRI was 3.5 years. The GTV-to-CTV margin used was 10 mm for six of seven participant institutions. One institution used 8 mm and three delineators (from three different institutions) used a margin of 5 mm. Conclusion: The data from this study suggests that appropriate guidelines, contouring quality assurance sessions, and training are still needed for the adoption of MR-based treatment planning for head and neck cancers. Such efforts should play a critical role in reducing inter-observer delineation variation and ensure standardization of target design across clinical practices.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".