Effects of ROI Placement on PET-Based Assessment of Tumor Response to Therapy
Bibliographic record
Abstract
Purpose. Quantitative PET response assessment during therapy requires regions of interest (ROI). Commonly, a fixed-size ROI is placed at the maximum uptake point in the pretreatment study. For intratreatment, the ROI is placed either at the maximum uptake point (ROIpeak) or at the same location as the pretreatment ROI (ROIsame). We have evaluated the effects of the ROI placement on response assessment. Methods. PET scans of 15 head and neck cancer patients were used to evaluate the effects of the two ROI methods on response assessment. Results. The average intratreatment ROIpeak uptake was 13.4% higher than the ROIsame uptake (range -14% to 38%). The average relative change in ROIpeak uptake was 7.9% lower than ROIsame uptake (range -5% to 36%), resulting in ambiguous tumour classification in 19% of the tumours. Conclusion. Quantitative PET response assessment using a fixed-size ROI is sensitive the ROI placement. The difference between ROIpeak and ROIsame could be substantial resulting in ambiguous response assessment. Although the fixed-size ROI is simple to implement, it is also prone to the limitations and should be used with caution. Clinical trial data are necessary to establish reliable thresholds for fixed-size ROI techniques and to evaluate their efficacy for response assessment.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".