WE‐FG‐206‐08: Pulmonary Functional Imaging Biomarkers of NSCLC to Guide and Optimize Functional Lung Avoidance Radiotherapy
Bibliographic record
Abstract
Purpose: Functional lung avoidance radiotherapy promises optimized therapy planning by minimizing dose to well‐functioning lung and maximizing dose to the rest of the lung. Patients with NSCLC commonly present with co‐morbid COPD and heterogeneously distributed ventilation abnormalities stemming from emphysema, airways disease, and tumour burden. We hypothesized that pulmonary functional imaging methods may be used to optimize radiotherapy plans to avoid regions of well‐functioning lung and significantly improve outcomes like quality‐of‐life and survival. To ascertain the utility of functional lung avoidance therapy in clinical practice, we measured COPD phenotypes in NSCLC patients enrolled in a randomized‐controlled‐clinical‐trial prior to curative intent therapy. Methods: Thirty stage IIIA/IIIB NSCLC patients provided written informed consent to a randomized‐controlled‐clinical‐trial ( https://clinicaltrials.gov/ct2/show/NCT02002052 ) comparing outcomes in patients randomized to standard or image‐guided radiotherapy. Hyperpolarized noble gas MRI ventilation‐defect‐percent (VDP) (Kirby et al, Acad Radiol, 2012) as well as CT‐emphysema measurements were determined. Patients were stratified based on quantitative imaging evidence of ventilation‐defects and emphysema into two subgroups: 1) tumour‐specific ventilation defects only (TSD), and, 2) tumour‐specific and other ventilation defects with and without emphysema (TSDVE). Receiver‐operating‐characteristic (ROC) curves were used to characterize the performance of clinical measures as predictors of the presence of non‐tumour specific ventilation defects. Results: Twenty‐one out of thirty subjects (70%) had non‐tumour specific ventilation defects (TSDVE) and nine subjects had ONLY tumour‐specific defects (TSD). Subjects in the TSDVE group had significantly greater smoking‐history (p=.006) and airflow obstruction (FEV1/FVC) (p=.001). ROC analysis demonstrated an 87% classification rate for smoking pack‐years, 90% for FEV1/FVC, and 56% for tumour RECIST measurements for identifying patients with non‐tumour and tumour‐specific ventilation abnormalities. Conclusion: 70% of NSCLC patients had ventilation abnormalities stemming from emphysema, airways disease and tumour burden. Smoking‐history and airflow obstruction, but not RECIST, identified NSCLC patients with ventilation abnormalities appropriate for functional lung avoidance therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".