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 18F‐FDG‐PET and CT perfusion (CTP). Methods Thirty‐one patients with early‐stage NSCLC participated in this prospective correlative study. Each underwent dynamic 18F‐FDG‐PET/CTP studies on a PET/CT scanner pre‐ and 8 weeks post‐SABR. The dynamic 18F‐FDG‐PET measured the tumour SUVmax, SUVmean and the following parameters: K1, k2, k3, k4 and Ki, 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 18F‐FDG‐PET and CTP study showed substantial and significant changes in SUVmax, SUVmean, k3, k4 and Ki. Significant decreases were evident in SUVmax (median 6.1 vs. 2.6; P < 0.001), SUVmean (median 2.5 vs. 1.5; P < 0.001), k3 (relative decrease of 52%; P = 0.002), Ki (relative decrease of 27%; P = 0.03), whereas there was an increase in k4 (+367%; P < 0.001). Conclusions Hybrid 18F‐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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".