Evaluating the robustness of dosiomics features over treatment planning parameters: a phantom-based study
Bibliographic record
Abstract
Dosimetric biomarkers, in terms of dosiomics features, play a crucial role in modeling radiotherapy and should be analyze d for their robustness and stability. This study aims to investigate how these dosiomics features will change over variations in treatment planning parameters. Different treatment plans were created by varying such parameters as field number, dose calculation algorithm, dose grid resolution, energy, monitor units, fraction, dose, field size, multileaf collimator, collimator angle, table angle, source-to-surface distance and source-to-axis distance, and wedge for a hypothetical tumor in the CIRS phantom CT scan. Dosiomics features were extracted with different segment sizes. The coefficient of variation (COV) was used to evaluate dosiomics feature changes with consider COV ≤ 5% as robust features. Our findings showed that many of the dosiomics features had significant variations due to changes in treatment parameters. First-order and gray-level co-occurrence matrix (GLCM) features were more stable (COV ≤ 5%) compared to others. Field and wedge changes had the most significant impact on features, while the dose calculation algorithm, dose, and MU changes had the lesser effects. Dosiomics features were vulnerable over changing treatment parameters and should always be reported. The GLCM features set was the most robust. Further studies are needed to identify robust dosiomics features for future biomarker discovery.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".