Inverse optimization of low‐cost kilovoltage x‐ray arc therapy plans
Bibliographic record
Abstract
PURPOSE: The objective of this work was to investigate the benefits of using inverse optimization treatment planning for kilovoltage arc therapy (KVAT) and to assess the dosimetric limitations of KVAT. METHODS: Monte Carlo (MC) calculated, inversely optimized KVAT plans of spherical, idealized breast, lung, and prostate lesions were calculated using the EGSnrc/BEAMnrc and DOSXYZnrc MC codes. The dose delivered with the KVAT system, which generates 200-225 kV photon beamlets, was calculated and inversely optimized using an optimization framework developed at McGill University. KVAT dose distributions were compared with inversely optimized and MC generated megavoltage (MV) volumetric modulated arc therapy (VMAT) plans as a reference. Prescription doses delivered to 95% of the planning target volume (PTV) were 38.5 (10 fractions), 60 (30 fractions) and 73.8 (41 fractions) Gy for the breast, lung and prostate patients, respectively. Dose distributions, dose volume histograms, and PTV homogeneity indices were used to evaluate KVAT and VMAT plans based on RTOG protocols. RESULTS: All organ-at-risk (OAR) doses were within prescribed dose limits for KVAT and VMAT plans. Generally, KVAT plans delivered higher doses to OARs. For example, due to the lower energy of KVAT, 50% of the rib volume received 12.9 Gy from KVAT while only receiving 2.5 Gy from VMAT. OAR doses were especially high for the KVAT prostate plan due to the presence of large volumes of bony anatomy, which illustrates a limitation of the KVAT system. The KVAT treatment times per fraction for the breast, lung and prostate patients were 2.8, 2.6 and 5.5 min, respectively. CONCLUSIONS: The inversely optimized KVAT plans presented in this work have demonstrated the ability of our novel low-cost, kilovoltage x-ray therapy system to safely treat deep-seated spherical lesions in breast and lung patients while meeting RTOG dose constraints on OARs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".