Thresholding in PET images of static and moving targets
Bibliographic record
Abstract
Continued therapeutic gain in the treatment of non-small-cell lung cancer (NSCLC) will depend upon our ability to escalate the dose to the primary tumour while minimizing normal tissue toxicity. Both these objectives are facilitated by the accurate definition of a target volume that is as small as possible. To this end, both tumour immobilizations via deep inspiratory breath-hold, along with positron emission tomography (PET), have emerged as two promising approaches. Though PET is an excellent means of defining the general location of a tumour focus, its ability to define exactly the geometric extent of such a focus strongly depends upon selection of an appropriate image threshold. However, in clinical practice, the image threshold is typically not chosen according to consistent, well-established criteria. This study explores the relationship between image threshold and the resultant PET-defined volume using a series of F-18 radiotracer-filled hollow spheres of known internal volumes, both static and under oscillatory motion. The effects of both image threshold and tumour motion on the resultant PET image are examined. Imaging data are further collected from a series of simulated gated PET acquisitions in order to test the feasibility of a patient-controlled gating mechanism during deep inspiratory breath-hold. This study illustrates quantitatively considerable variability in resultant PET-defined tumour volumes depending upon numerous factors, including image threshold, size of the lesion, the presence of tumour motion and the scanning protocol. In this regard, when using PET in treatment planning for NSCLC, the radiation oncologist must select the image threshold very carefully to avoid either under-dosing the tumour or overdosing normal tissues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".