Poster - Thurs Eve-22: Image guided radiation therapy for lung cancer
Bibliographic record
Abstract
OBJECTIVE: To determine the geometric accuracy of conventional and stereotactic lung radiotherapy using cone-beam CT image guidance, and assess the efficacy of these image-guided radiation therapy (IGRT) processes. MATERIALS AND METHODS: IGRT was first used for our stereotactic lung program, where high geometric accuracy is required to deliver high doses in few fractions. The initial positional accuracy for 47 patients was assessed by registering daily CBCT to the planning CT; the patient position was corrected when the CBCT indicated discrepancies > ± 3 mm in any direction. For 19 of these patients, a second CBCT was acquired to assess the residual error. IGRT was also used to assess the initial and residual errors for lung cancer patients treated conventionally with (14 pts; 584 CBCT) and without (25 pts; 1032 CBCT) a remote-controlled treatment couch. Systematic (Σ) and random (σ) positional errors were assessed for these three groups. RESULTS: For stereotactic lung patients, Σ and σ ranged between 4.1 and 6.1 mm. IGRT reduces these errors to 1.2-1.9 mm, raising the proportion of patients within ± 3 mm from 16% to 82%. For conventional lung cancer patients, Σ and σ ranged between 1.4 and 3.8 mm, and IGRT raises the proportion of patients within ± 3 mm from 27% to 67%, with the remote-controlled couch further improving this proportion to 84%. CONCLUSION: IGRT clearly confirms the high geometric accuracy required for stereotactic lung patients. This new paradigm has been transported to patients with locally-advanced lung cancer, with similar accuracy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.019 |
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".