Abstract IA-22: Automated treatment planning and quality assurance in radiation oncology
Bibliographic record
Abstract
Abstract The delivery of radiation for the treatment of cancer is a complicated process that requires both clinical and technical expertise to ensure radiation treatments are safe and effective. Sub-optimal radiation treatments have the potential to result in significant detriment to the patient and several studies have shown radiation treatments, which deviate from established clinical guidelines, result in worse patient outcomes. Therefore, the current radiation treatment process requires substantial multi-disciplinary resources to both generate and verify radiation treatments are of high-quality. In this talk, a previously validated machine learning platform customized for radiation oncology will be presented. The method automatically learns based on data from thousands of previously treated patients which relationships and patterns in radiation oncology image and treatment data and has been applied for automated data mining activities, automated quality assurance to support expedited radiation treatment review, and for radiation dose prediction to develop new radiation treatments by best deciding where dose should be placed and how dose should be delivered without requiring any manual intervention. Therefore, the method can be used to both generate personalized radiation treatments and to quantitatively score radiation treatments for quality and classify radiation treatments that have errors. The automated platform can readily be integrated into current clinical process to improve efficiency in the radiation treatment planning and plan review processes and to better utilize the vast data we have to ensure we are providing patients with highly personalized radiation treatments. Citation Format: Thomas G. Purdie. Automated treatment planning and quality assurance in radiation oncology [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-22.
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.
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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".