Evaluating the robustness of dosiomics features over treatment planning parameters: a phantom-based study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it