Monte Carlo calculated kilovoltage x-ray arc therapy plans for three lung cancer patients
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it