Heuristics for the direct aperture optimisation in intensity modulated radiotion therapy: a systematic literature review
Bibliographic record
Abstract
Intensity-modulated radiation therapy (IMRT) is an advanced technique for cancer treatment that uses a computer-controlled linear accelerator to customise beams’ radiation intensities for patients, optimising the treatment effectiveness. The complexity of IMRT planning requires sophisticated algorithms to solve the different optimisation problems that arise in the context of IMRT treatment planning. One of those optimisation problems is the Direct Aperture Optimisation (DAO). The DAO problem aims to find a set of aperture shapes for each beam angle to enhance precision and improve clinical outcomes. However, this process is computationally intensive and thus, heuristic approaches have been proposed to balance computational efficiency and solution quality, offering nearly optimal solutions within clinically acceptable times. This systematic literature review aims to trace the development and application of heuristic algorithms for the DAO problem in the context of IMRT over the past two decades. We synthesised 41 studies published between 2002 and 2023, sourced from seven major databases (ACM, IEEE Xplore, PubMed, ScienceDirect, Springer, Scopus, and Web of Science). The review highlights key trends, innovations, and future directions in using heuristic methods for DAO, providing valuable insights for researchers and practitioners in radiotherapy optimisation.
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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".