Decision‐making models in the analysis of portal films: A clinical pilot study
Bibliographic record
Abstract
Portal films continue to play an important role in the verification of radiotherapy treatment. There is still some discussion, however, as to what action should be taken after a port film has shown a radiation field deviation from the prescribed volume. It was the aim of the present pilot study to investigate the performance of three decision-making models ('Amsterdam', 'Quebec' and 'Newcastle') and an expert panel basing their decision on intuition rather than formal rules after portal film acquisition in a clinical setting. Portal films were acquired on every day during the first week of treatment for five head and neck and five prostate cancer patients (diagnostic phase). If required, the field position was modified according to our normal practice following the recommendation of the expert panel. In order to analyse the results of the models, however, additional port films were taken in the following 3 treatment weeks with the patient moved as required by the different models (intervention phase). The portal films were taken over 4 consecutive days, positioning the patient according to each of the different models on one day each. None of the models diagnosed a field misplacement in the head and neck patients, while the 'Amsterdam' and 'Quebec' models predicted a move in one prostate patient. The 'Newcastle' model, which is based on Hotelling's T2 statistic, proved to be more sensitive and diagnosed a systematic displacement for three prostate patients. The intervention phase confirmed the diagnosis of the model, even if the three portal films taken with the patient position adjusted as required by the model proved to be insufficient to demonstrate an improvement. The 'Newcastle' model does not rely on assumptions about the random movement of patients and requires five portal films before a decision can be reached. This approach lends itself well to incorporation into electronic portal imaging 'packages', where repeated image acquisitions present no logistical difficulty.
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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 | 0.020 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".