Assessment of tumour response after stereotactic ablative radiation therapy for lung cancer: A prospective quantitative hybrid <sup>18</sup>F‐fluorodeoxyglucose‐positron emission tomography and <scp>CT</scp> perfusion study
Bibliographic record
Abstract
Abstract Introduction Stereotactic ablative radiotherapy ( SABR ) is a guideline‐recommended treatment for inoperable stage I non‐small cell lung cancer ( NSCLC ), but imaging assessment of response after SABR is difficult. The goal of this study was to evaluate imaging‐based biomarkers of tumour response using dynamic 18 F‐ FDG ‐ PET and CT perfusion ( CTP ). Methods Thirty‐one patients with early‐stage NSCLC participated in this prospective correlative study. Each underwent dynamic 18 F‐ FDG ‐ PET / CTP studies on a PET / CT scanner pre‐ and 8 weeks post‐ SABR . The dynamic 18 F‐ FDG ‐ PET measured the tumour SUV max , SUV mean and the following parameters: K 1 , k 2 , k 3 , k 4 and K i , all using the Johnson–Wilson–Lee kinetic model. CTP quantitatively mapped BF , BV , MTT and PS in tumours and measured largest tumour diameter. Since free‐breathing was allowed during CTP scanning, non‐rigid image registration of CT images was applied to minimize misregistration before generating the CTP functional maps. Differences between pre‐ and post‐ SABR imaging‐based parameters were compared. Results Tumour size changed only slightly after SABR (median 26 mm pre‐ SABR vs. 23 mm post‐ SABR ; P = 0.01). However, dynamic 18 F‐ FDG ‐ PET and CTP study showed substantial and significant changes in SUV max , SUV mean , k 3 , k 4 and K i . Significant decreases were evident in SUV max (median 6.1 vs. 2.6; P < 0.001), SUV mean (median 2.5 vs. 1.5; P < 0.001), k 3 (relative decrease of 52%; P = 0.002), K i (relative decrease of 27%; P = 0.03), whereas there was an increase in k 4 (+367%; P < 0.001). Conclusions Hybrid 18 F‐ FDG ‐ PET / CTP allowed the response of NSCLC to SABR to be assessed regarding metabolic and functional parameters. Future studies are needed, with correlation with long‐term outcomes, to evaluate these findings as potential imaging biomarkers of response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".