Sci-Thur AM: Planning - 09: Assessing dynamic IMRT field modulation in prostate plans
Bibliographic record
Abstract
In a previous study, the variogram fractal dimension (FD) method was found to be very accurate at identifying planned head and neck IMRT fields that are overly-modulated. In the current study, the authors used MATLAB® to develop FracMod, a graphical user interface (GUI) and variogram FD analysis tool to assess modulation complexity of dynamic IMRT fields designed for treatments of the prostate alone and prostate plus pelvic nodes. A set of 5 prostate plans (25 fields) and 5 prostate plus pelvic node plans (35 fields) were used to choose FD cut-points that ensure no false positives (100% specificity) in distinguishing between moderate field modulation (typical modulation used clinically at the authors' institution) and high modulation. Field modulation was controlled by adjusting fluence smoothing parameters in the Eclipse™ treatment planning system. The area under the curve (AUC) from receiver operating characteristic (ROC) analysis was used to quantitatively compare the ability of FD and the number of monitor units (MUs) for distinguishing between the moderate and high modulation fields. The variogram FD method gave AUCs of 0.96 (almost perfect classification) and 1.00 (perfect classification) for the prostate alone and the prostate plus pelvic node fields, respectively. The variogram FD method is an accurate metric; performing better than the number of MUs at identifying high modulation IMRT fields planned for the treatment of prostatic carcinoma. Hence, FracMod will enable Radiotherapy Physicists to easily and accurately quantify the degree of modulation of IMRT fields and adjust overly-modulated fields at the treatment planning stage.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".