MO-G-201-02: Comparing Sample Size Requirements for Knowledge-Based Treatment Planning
Bibliographic record
Abstract
Purpose: To compare how training set size affects the accuracy of a knowledge-based planning (KBP) model applied to prostate and head and neck (HN) cancer. Methods: We selected a KBP model from the literature that uses distance-to-target histograms and organ volumes to predict an achievable dose-volume-histogram (DVH) curve for each organ-at-risk (OAR). We trained both the prostate and HN model using training set sizes of n=10, 20, 30, 50,75, and 100. We set aside 100 randomly selected treatment plans from each of the two respective cohorts of 218 to serve as a validation set for all experiments. For each value of n, we randomly selected 100 different training sets with replacement from the remaining 118 plans. Each of the 100 training sets was used to train a model for each value of n and for both prostate and HN. To evaluate the models we predicted DVH curves for each of the 100 plans in the validation set. To estimate the minimum required sample size, we used the rank-sum test to determine if the median error for each sample size from 10 to 75 was equal to the median error for the maximum sample size of 100. Results: In general, larger sample sizes were required for HN compared to prostate. For prostate, a minimum training set size of 30 plans was needed to accurately predict the bladder DVH, while at least 75 plans were needed for the rectum. For HN, the minimum training set size was 100 for the larynx esophagus and spinal cord, 75 for the left parotid and mandible, and only 50 for the right parotid. Conclusion: The minimum sample size required for accurate treatment plan generation using KBP is OAR and site dependent. Adequate sample sizes are essential for successful clinical implementation of KBP models. This research was funded in part by the Natural Sciences and Engineering Research Council of Canada.
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".