Sci-Thurs PM: Delivery-07: Evaluation of prospects to use daily megavoltage CT studies for adaptive radiotherapy
Bibliographic record
Abstract
PURPOSE: To evaluate gross tumor volume (GTV) changes for non-small cell lung cancer (NSCLC) patients using daily megavoltage CT (MVCT) studies acquired before each treatment fraction on helical tomotherapy, and to relate the potential benefit of adaptive image-guided radiotherapy to changes in GTV. METHODS: 17 patients were prescribed 30 fractions of radiotherapy on helical tomotherapy for NSCLC at London Regional Cancer Program from December 2005 to March 2007. The GTV was contoured on the daily MVCT studies of each patient. Adapted plans were created using merged MVCT-kVCT image sets to investigate the advantages of replanning for patients with differing GTV regression characteristics. RESULTS: The average GTV change observed over 30 fractions was -38%, ranging from -12 to -87%. No significant correlation was observed between GTV change and patient's physical or tumor features. The pattern of GTV changes of the 17 patients could be broadly divided into 3 groups with distinctive potential for benefit from adaptive planning. CONCLUSIONS: GTV changes are difficult to predict quantitatively based on patient or tumor characteristics. If changes do occur, there are points in time during the treatment course when it may be appropriate to adapt the plan to improve sparing of normal tissues. If the GTV decreases by greater than 30% at any point in the first twenty fractions of treatment, adaptive planning is appropriate to further improve the therapeutic ratio.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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) | 0.004 | 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".