Prospective multi‐institutional study of library‐based adaptive radiotherapy for cervical cancer: Evaluation of plan‐of‐the‐day selection and population analysis
Bibliographic record
Abstract
PURPOSE: Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, with 3D daily imaging guiding the plan selection. In a phase II multi-institutional trial of cone-beam CT (CBCT)-guided PoD-ART for locally advanced cervical carcinoma (LACC), this study aimed at evaluating the PoD selection, its geometric and dosimetric impact and characterizing a sub-population of patients associated with dosimetric improvement from ART. MATERIAL AND METHODS: For 49 cervical cancer patients, three planning CT scans [empty bladder (EB), intermediate bladder (IB) and full bladder (FB)] were acquired to generate a treatment plan library. A dose of 45 Gy was prescribed to the planning target volume in 25 fractions. Daily CBCT were acquired to visually select the best plan in the library (Manual-ART strategy). A deep learning model was used to segment daily clinical target volume (CTVt) and organs-at-risk (OAR). Manual-ART was compared to two strategies: (i) "Non-ART" strategy (IB-CT treatment plan only); (ii) PoD-ART strategy selecting the PoD maximizing CTVt coverage ("Cov-ART"). Geometrical and dosimetric coverages of daily CTVt and OAR were assessed. Decision trees were developed to predict the subpopulation of patients associated with dosimetric benefit from PoD-ART. RESULTS: The agreement in PoD selection between Manual-ART and Cov-ART was 63.5%. Compared to the Non-ART strategy (D95%-CTV: 43.6 ± 4.1 Gy), PoD-ART significantly increased the dose to the target, with Manual-ART achieving 44.0 ± 3.0 Gy and Cov-ART with 44.1 ± 2.0 Gy. Decision trees using IB-CT plan and first two treatment fractions correctly classified 85.4% and 93.8% of patients as benefiting or not from PoD-ART. CONCLUSIONS: In PoD-ART for LACC, selected treatment plans by the radiation oncologist had 63.5% concordance with treatment plans maximizing target coverage. PoD-ART increased dose to target, without compromising dose to OARs, with the largest benefit observed in a sub-population identifiable after two treatment fractions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".