EP-1181: SBRT in oligometastatic/oligorecurrent NSCLC cancer patients: a new therapeutic approach
Bibliographic record
Abstract
Purpose/Objective: Pulmonary emphysema (PE) is a non functional lung region due to destruction of the gasexchanging tissues.This information is never integrated into the radiotherapy (RT) plan for lung cancer (LC).With Myrian © software dedicated to radiology, an automatic delineation of PE can be processed and transferred to the treatment planning system (TPS).The main goal of this study was to evaluate the possibility to transfer and integrate the PE information into a radiotherapy plan, and calculate the volume of the healthy lung. Materials and Methods:We analyzed patients with LC and PE treated with RT excluding stereotactic treatments.All patients had a diagnostic CT before RT.The PE structure was delineated with Myrian © on the diagnostic CT and then transferred to the TPS.To permit a good correlation a deformable registration was performed between the diagnostic CT and the CT dedicated to RT with Velocity AI © .We created the healthy lung (HL) structure corresponding to the lung excluding the planning treatment volume (PTV) and the PE.We compared for each patient the lung-PTV volume to the HL volume.Results: Nine patients were included in our study, 2 women and 7 men.They all had intensity modulated RT.Mean total dose was 52.7 Gy (range, 36 -66).The delay between the diagnostic and the planning CT varied between 3 to 111 days.The mean PE volume was 322 cm 3 (range, 12 -1394), and the mean variation between the lung-PTV volume and HL volume was 7.25% (range, 0.33% -26.22%).Two patients had major volume variations respectively 14.8% and 26.22% corresponding to a PE volume of 668 and 1394 cm 3 . Conclusions:The transfer of the PE structure previously delineated with Myrian © to the TPS is readily feasible.Moreover, the difference between the HL and the lung-PTV structures can be considerable.It will be of interest to analyze the impact of integrating PE information to the radiotherapy plan to spare more HL for patients with PE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".