Molecular imaging of biologic characteristics and drug uptake : towards personalized medicine using dose painting
Bibliographic record
Abstract
Background and PurposeNon-small cell lung cancer (NSCLC) tumours are mostly heterogeneous.We hypothesized that areas within the tumour with a high pre-radiation 18 F-deoxyglucose (FDG) uptake, could identify residual metabolic-active areas, ultimately enabling selectiveboosting of tumour sub-volumes. Material and MethodsFifty-five patients with inoperable stage I-III NSCLC treated with chemo-radiation or with radiotherapy alone were included.For each patient one pre-radiotherapy and one post-radiotherapy FDG-PET-CT scans was available.Twenty-two patients showing persistent FDG-uptake in the primary tumour after radiotherapy were analyzed.Overlapfractions (OF) were calculated between standardized uptake value (SUV) thresholdbased auto-delineations on the pre-and post-radiotherapy scan. ResultsPatients with residual metabolic-active areas within the tumour had a significantly worse survival compared to individuals with a complete metabolic response (p=0.002).The residual metabolic-active areas within the tumour largely corresponded (OF>70%) with the 50%SUV high FDG-uptake area of the pre-radiotherapy scan.The hotspot within the residual area (90%SUV) was completely within the GTV (OF=100%), and had a high overlap with the pre-radiotherapy 50%SUV threshold (OF>84%). ConclusionThe location of residual metabolic-active areas within the primary tumour after therapy corresponded with the original high FDG-uptake areas pre-radiotherapy.Therefore, a single pre-treatment FDG-PET-CT scan allows for the identification of residual metabolic-active areas.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".