Poster — Thur Eve — 54: A software solution for ongoing DVH quality assurance in radiation therapy
Bibliographic record
Abstract
PURPOSE: A program has been developed in MATLAB for use in quality assurance of treatment planning of radiation therapy. It analyzes patient DVH files and compiles dose volume data for review, trending, comparison and analysis. MATERIAL AND METHODS: Patient DVH files are exported from the Eclipse treatment planning system and saved according to treatment sites and date. Currently analysis is available for 4 treatment sites; Prostate, Prostate Bed, Lung, and Upper GI, with two functions for data report and analysis: patient-specific and organ-specific. The patient-specific function loads one patient DVH file and reports the user-specified dose volume data of organs and targets. These data can be compiled to an external file for a third party analysis. The organ-specific function extracts a requested dose volume of an organ from the DVH files of a patient group and reports the statistics over this population. A graphical user interface is utilized to select clinical sites, function and structures, and input user's requests. RESULTS: We have implemented this program in planning quality assurance at our center. The program has tracked the dosimetric improvement in GU sites after VMAT was implemented clinically. It has generated dose volume statistics for different groups of patients associated with technique or time range. CONCLUSION: This program allows reporting and statistical analysis of DVH files. It is an efficient tool for the planning quality control in radiation therapy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.022 |
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".