SU‐EE‐A4‐05: Individual Target Volume Definition in NSCLC Using PET
Bibliographic record
Abstract
Purpose: To determine whether quantitatively segmented PET images could be used to identify the volume containing a tumor and its total motion. If possible, PET could provide individualized internal target volumes (ITV) in lung cancer. Method and Materials: A physiological phantom containing background level of Na‐22 was used. Two spheres filled with 0.5 mCi/ml of Na‐22 were used to simulate tumors; each was oscillated within one lung of the phantom with 4 preset motion extents in S/I, A/P, and M/L directions. PET and CT imaging were performed on an integrated PET/CT scanner. A CT‐based GTV was generated using a threshold of −850 HU. A population‐based margin of 15 mm, reflecting both motion and set‐up uncertainties, was added to generate a CT‐based PTV. A PET‐based ITV was defined using a threshold of three standard deviations above normal lung background. A set‐up margin of 7.5 mm was added to PET‐based ITVs to create PTVs. Image‐based PTVs were compared to ideal PTVs. Clinical validation of this methodology was performed on 7 patients with parenchymal lung lesions with the addition of digital fluoroscopy. 18‐FDG was used for patient PET scanning. Results: For the phantom study, PET‐based PTVs were closer to the ideal PTV than those based on CT. While the PET‐based PTVs were approximately half the size of the CT‐based PTVs, in no case would the PET‐based PTVs have resulted in geographical miss. For majority of the patients, PET accurately predicted or slightly over‐predicted the tumor motion extents compared to fluoroscopy; differences were within 2 voxels. Conclusion: Based on the phantom study and initial clinical validation, we have found that quantitatively segmented PET images can provide an accurate individualized ITV that correlates with a tumor and its motion. Conflict of Interest: Research was supported by NCI Canada with funds from Ontario Cancer Society.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".