Image-guided lung radiotherapy: Bringing technology into routine clinical practice
Bibliographic record
Abstract
18093 Background: Cone-beam CT (CBCT), an imaging system integrated into the RT treatment unit, produces 3D images far superior to the conventional 2D portal images used for verification of patient (pt) set-up. This allows direct matching to the RT treatment planning CT images, potentially increasing the precision of RT delivery. We report on the broad implementation of this new RT image-guided paradigm in lung cancer patients at our center. Methods: All lung cancer pts undergoing radical RT were planned using 4DCT and imaged daily for repositioning with CBCT since 04/06. Initially, CBCT datasets were compared with the planning CT to assess the setup error (bone surrogate), using two immobilisation methods: evacuated bags (EB) and chest-board (ChB). Discrepancies >3 mm between the two datasets, in any direction, were corrected before the start of each RT fraction. Data were retrospectively analyzed to assess the initial and residual discrepancies (43 pts; 1,128 CBCTs).Alternative matching strategies were also tested (carina & tumor) using both manual and automatic methods (30 pts). Protocols had REB approval. Results: In total, 657 (58%) RT treatments required adjustment after initial positioning on the treatment couch. The two immobilization methods were equivalent (p=0.18); the mean pt shift required for ChB pts was 55±18mm and for EB pts was 69±32mm. Given that residual uncertainties were <3 mm, margin calculations reveal that large reductions (54–79%) may be possible for tumors not influenced by respiratory motion. The performance of automatic matching was reasonable for carina (correlation [r] 0.8–0.84) and bone (r 0.58–0.81) but discrepancies were seen for tumor (r 0.63–0.69). Conclusions: Daily CBCT provides greatly increased accuracy of set-up, to within 3 mm of the planned bony anatomy, which may improve tumor control by confirming geographic accuracy. The role of image-guided RT in reducing the volume of irradiated normal tissue may play an important role in addressing toxicity concerns associated with combined modality treatment and facilitate safe RT dose escalation. In addition to increasing RT precision, daily CBCT allows routine visualisation of the tumor as well as to bony anatomy, presenting an exciting opportunity to adapt the treatment plan based on an individual response. [Table: see text]
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".