Towards Conformal Interstitial Light Therapies: Modelling Parameters, Dose Definitions and Computational Implementation
Bibliographic record
Abstract
Simulated Annealing, Theory with Applications 52therapies and the clinical utility of simulated annealing in planning for these novel treatment options. Simulated annealing in radiotherapySimulated annealing has been used as an optimization technique for radiation treatment planning in the clinical setting, with successes reported in both external beam radiation therapy (EBRT) (Aubry et al., 2006;Beaulieu et al., 2004;Morrill et al., 1995) and high dose-rate (HDR) brachytherapy (Lessard & Pouliot, 2001;Martin et al., 2007).For EBRT, the basic optimization problem is the determination of the appropriate temporal and spatial arrangements of multiple external radiation beams, which can be tailored in highly sophisticated and precise ways in modern 3-D conformal radiation therapy.For HDR-brachytherapy (which delivers a high radiation dose directly by implanting intense radioactive sources within the tumour for a short time), the optimization involves adjusting the duration (or dwell time) that a source pauses or dwells at each position (called dwell position) along the implanted catheter.Treatment delivery is accomplished using a computer-controlled robotic unit called a stepping source device or an afterloader, which moves the radioactive sources (commonly 192 Ir) along the catheters according to the optimized dwell time distribution in order to deliver the desired radiation dose distribution.There is now a shift in paradigm as we strive to achieve the century-old objective of delivering a curative radiation dose to the tumour while sparing sensitive structures and surrounding normal tissues.That is, instead of manually specifying the treatment parameters and repeatedly evaluating the resulting radiation dose distribution (forward planning), a desired dose distribution is prescribed by the physician and the task of finding the appropriate treatment parameters is automated with an optimization algorithm (inverse planning).The latter approach, or inverse planning, is much more goal-oriented and efficient.The concept of inverse planning, using simulated annealing as the optimization engine, can be briefly summarized as follows.Note that the following description focuses on HDRbrachytherapy and is greatly simplified, although the steps are quite similar for EBRT.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".