Sci‐Sat AM (2) Therapy‐06: Clinical experience with adaptive radiation therapy for lung cancer with tomotherapy
Bibliographic record
Abstract
Helical tomotherapy (TomoTherapy Inc., Madison, WI) is a new form of image‐guided radiation therapy that combines features of a linear accelerator and a helical CT scanner. Helical tomotherapy allows megavoltage computer tomography (MVCT) imaging of the patient immediately prior to treatment. The MVCT images are compared to the treatment planning (kilovoltage) CT images at the operator station in order to ensure correct alignment to the planning target volume and critical structures. We report clinical experience with our technique for adaptive therapy in two lung cancer cases where the delivery fluence modifications were considered imperative due to concerns regarding changes in patient anatomy. Daily MVCT imaging with helical tomotherapy co‐registered with the planning kVCT study can provide feedback allowing correction of patient position due to systematic or random error or organ motion. In the case of lung cancer, the gross tumour volume is clearly visible on MVCT due to high tissue contrast in the lung facilitating daily positioning correction for small setup or organ location variations. If the target has dramatically changed in its shape or location and/or other major anatomy changes occur, the MVCT study can be used for re‐calculation of the expected dose distribution which is then compared to the originally planned one. If the difference is considered clinically significant, a repeat kVCT study can be performed and a new plan is developed for further treatment, thus allowing the patient specific adaptive radiotherapy to account for anatomic changes during treatment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".