Threshold modification for tumour imaging in non-small-cell lung cancer using positron emission tomography
Bibliographic record
Abstract
AIM: Positron emission tomography (PET) has been used increasingly in the staging and radiotherapy treatment planning of non-small-cell lung cancer (NSCLC). This study investigates the factors that affect the resultant size of a given image on PET. METHODS: PET was used to assess the geometric characteristics of a series of radioisotope-filled, stationary spheres of known volume, surrounded by positron-emitting radioactive tracer of variable activity. The resultant PET-derived spherical volumes were then referenced to the known spherical volumes in order to illustrate quantitatively the potential influence of image threshold, tumour size and background concentration. This influence was further illustrated by clinical examples. RESULTS: Considering the diameter of the spheres used in this study (10-48 mm), higher image thresholds were required for accurate rendering of the smallest spherical volumes. This inverse relationship was most consistently illustrated at the lowest background intensity ratios. CONCLUSION: PET-derived volumes of NSCLC must be interpreted with caution. The data presented in this study may be used to guide the selection of appropriate image thresholds for potential clinical application.
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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 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".