Poster — Thur Eve — 20: Serial FDG 4DPET imaging during radiotherapy in advanced lung cancer patients
Bibliographic record
Abstract
The availability of respiratory synchronized PET (4DPET) imaging has enabled more accurate analysis of metabolic response since motion blur is minimized. We present our preliminary analysis of serial FDG 4DPET images acquired at weeks 0, 2, 4, and 7 during radiotherapy of seven stage II–III NSCLC patients. The tumor and nodal PTV of the week 0 images restrained a 4DPET image thresholding algorithm to automatically contour SUV levels ranging from 20 to 80% of the maximum SUV, creating an intensity volume histogram (IVH) for each week. These contours allowed analysis of PET volumes and standard PET metrics such as SUVmax and SUVmean. We found a trend for decreasing SUVmax and SUVmean over a treatment course in both the tumor and nodal regions. On average, the SUVmax within the tumor decreased by 17±13% (1 SD) after 2 weeks, 30±13% after 4 weeks, and 39±19% after 7 weeks of radiotherapy. Decreasing volume trends were also observed in the 20 to 80% max SUV autocontours, ranging from 26±29% to 50±40% respectively, over 7 weeks of treatment. Only one patient demonstrated an increase in FDG uptake within the tumor volume between week 0 and week 2 of treatment, and was also the only patient to recur locally at 3 months following treatment. Changes in tumor metabolism over the course of advanced NSCLC radiotherapy are quantifiable with serial FDG 4DPET imaging. Preliminary analysis suggests that variations in these trends could be useful in identifying non‐responding patients that may require an alternative radiotherapeutic approach.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".