Monte Carlo calculated kilovoltage x-ray arc therapy plans for three lung cancer patients
Bibliographic record
Abstract
Abstract Purpose : The intent of this work was to evaluate the ability of our 200 kV kilovoltage arc therapy (KVAT) system to treat realistic lung tumors without exceeding dose constraints to organs-at-risk (OAR). Methods and Materials : Monte Carlo (MC) methods and the McO optimization framework generated and inversely optimized KVAT treatment plans for 3 SABR lung cancer patients. The KVAT system was designed to treat deep-seated lesions with kilovoltage photons. KVAT delivers dose to roughly spherical PTVs and therefore non-spherical PTVs were divided into spherical sub-volumes. A prescription dose of 12 Gy/fx × 4 fractions was planned to 90% of the PTV volume. KVAT plans were compared to VMC++ calculated, 6 MV stereotactic ablative radiotherapy (SABR) treatment plans. Dose distributions, dose volume histograms, gradient index (GI), planned mean doses and plan treatment times were calculated. Dose constraints for organs-at-risk (OAR) were taken from RTOG 101. Results : All plans, with the exception of the rib dose calculated in one of the KVAT plans for a peripheral lesion, were within dose-constraints. In general, KVAT plans had higher planned doses to OARs. KVAT GI values were 5.7, 7.2 and 8.9 and SABR values were 4.6, 4.1, and 4.7 for patient 1, 2 and 3, respectively. KVAT plan treatment times were 49, 65 and 17 min for patients 1, 2 and 3, respectively. Conclusions : Inverse optimization and MC methods demonstrated the ability of KVAT to produce treatment plans without exceeding TG 101 dose constraints to OARs for 2 out of 3 investigated lung cancer patients.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".